--- title: GLM-4.7 metatags: description: "Deploy GLM-4.7 with SGLang on NVIDIA Blackwell (B200, GB200) and AMD GPUs - state-of-the-art reasoning, robust tool calling, and NVFP4 weights for Blackwell." --- ## 1. Model Introduction [GLM-4.7](https://huggingface.co/zai-org/GLM-4.7) is a powerful language model developed by Zhipu AI, featuring advanced capabilities in reasoning, function calling, and agent workflows. GLM-4.7 brings improvements across all major domains: - **Extended Context Window**: Expanded context window supporting even longer documents and complex multi-turn conversations - **Enhanced Reasoning**: Improved reasoning capabilities with better chain-of-thought processing - **Superior Coding**: Significantly improved code generation and understanding, with better real-world application performance - **Advanced Tool Use**: More robust tool calling and agent capabilities for complex workflows - **Optimized Performance**: Better throughput and latency characteristics across all hardware platforms For more details, please refer to the [official GLM-4.7 documentation](https://docs.z.ai/guides/llm/glm-4.7). **Key Features:** - **State-of-the-Art Reasoning**: Enhanced reasoning capabilities for the most complex problem-solving tasks - **Multiple Quantizations**: BF16, FP8, and NVFP4 variants for different performance/memory trade-offs - **Hardware Optimization**: Tuned for NVIDIA Blackwell (B200, GB200) and AMD MI300X/MI325X/MI355X GPUs - **High Performance**: Optimized for both throughput and latency scenarios **Available Models:** - **BF16 (Full precision)**: [zai-org/GLM-4.7](https://huggingface.co/zai-org/GLM-4.7) - **FP8 (8-bit quantized)**: [zai-org/GLM-4.7-FP8](https://huggingface.co/zai-org/GLM-4.7-FP8) - **NVFP4 (4-bit, NVIDIA Blackwell)**: [nvidia/GLM-4.7-NVFP4](https://huggingface.co/nvidia/GLM-4.7-NVFP4) **License:** Please refer to the [official GLM-4.7 model card](https://huggingface.co/zai-org/GLM-4.7) for license details. ## 2. SGLang Installation SGLang offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements. Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions. **Docker Images by Hardware Platform:**

Hardware Platform	Docker Image
NVIDIA H100 / H200 / B200	`lmsysorg/sglang:v0.5.12`
NVIDIA GB200 / B300 / GB300 (aarch64)	`lmsysorg/sglang:v0.5.12-cu130`
AMD MI300X / MI325X	`lmsysorg/sglang:v0.5.12-rocm720-mi30x`
AMD MI355X	`lmsysorg/sglang:v0.5.12-rocm720-mi35x`

## 3. Model Deployment This section provides deployment configurations optimized for different hardware platforms and use cases. ### 3.1 Basic Configuration **Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform, quantization method, deployment strategy, and thinking capabilities. import { GLM47Deployment } from "/src/snippets/autoregressive/glm-47-deployment.jsx"; ### 3.2 Configuration Tips Pick a weight format by hardware: **NVFP4** on NVIDIA Blackwell (B200, GB200), **FP8** on H100/H200/AMD, **BF16** as the full-precision fallback. The recommended tensor-parallel size per platform:

Hardware	NVFP4	FP8	BF16
B200 (8×, single node)	tp=2 / 4 / 8	tp=4 / 8	tp=8
GB200 (NVL72, 4× per tray)	tp=2 / 4	tp=4	—
H200 (8×)	—	tp=8	tp=8
AMD MI300X / MI325X / MI355X	—	tp=2 / 4 / 8	tp=4 / 8

- **EAGLE Speculative Decoding:** Supported for GLM-4.7. Add `--speculative-algorithm EAGLE --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4`. The spec-v2 overlap scheduler is enabled by default; pass `--disable-overlap-schedule` to disable. Enable via the interactive command generator above. - **Thinking Budget:** Use `--enable-custom-logit-processor` flag and pass `Glm4MoeThinkingBudgetLogitProcessor` in requests to cap the model's thinking token count (see section 4.2.3). For general GLM-4.x family launch guidance (AMD ROCm notes and more), see [Launch GLM-4.5 / GLM-4.6 / GLM-4.7 with SGLang](/cookbook/autoregressive/GLM/GLM-4.5). Per-hardware bench commands and flags are inline in §5.1 below. ## 4. Model Invocation ### 4.1 Basic Usage For basic API usage and request examples, please refer to: - [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request) ### 4.2 Advanced Usage #### 4.2.1 Reasoning Parser GLM-4.7 supports Thinking mode by default. Enable the reasoning parser during deployment to separate the thinking and the content sections: ```shell Command python -m sglang.launch_server \ --model zai-org/GLM-4.7 \ --reasoning-parser glm45 \ --tp 8 \ --host 0.0.0.0 \ --port 8000 ``` **Streaming with Thinking Process:** ```python Example from openai import OpenAI client = OpenAI( base_url="http://localhost:8000/v1", api_key="EMPTY" ) # Enable streaming to see the thinking process in real-time response = client.chat.completions.create( model="zai-org/GLM-4.7", messages=[ {"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"} ], temperature=0.7, max_tokens=2048, stream=True ) # Process the stream has_thinking = False has_answer = False thinking_started = False for chunk in response: if chunk.choices and len(chunk.choices) > 0: delta = chunk.choices[0].delta # Print thinking process if hasattr(delta, 'reasoning_content') and delta.reasoning_content: if not thinking_started: print("=============== Thinking =================", flush=True) thinking_started = True has_thinking = True print(delta.reasoning_content, end="", flush=True) # Print answer content if delta.content: # Close thinking section and add content header if has_thinking and not has_answer: print("\n=============== Content =================", flush=True) has_answer = True print(delta.content, end="", flush=True) print() ``` **Output Example:** ```text Output =============== Thinking ================= To solve this problem, I need to calculate 15% of 240. Step 1: Convert 15% to decimal: 15% = 0.15 Step 2: Multiply 240 by 0.15 Step 3: 240 × 0.15 = 36 =============== Content ================= The answer is 36. To find 15% of 240, we multiply 240 by 0.15, which equals 36. ``` **Note:** The reasoning parser captures the model's step-by-step thinking process, allowing you to see how the model arrives at its conclusions. #### 4.2.2 Tool Calling **Parser names by model:** GLM-4.5 and GLM-4.6 use `--tool-call-parser glm45`. GLM-4.7 and GLM-4.7-Flash use `--tool-call-parser glm47`. All GLM models use `--reasoning-parser glm45` regardless of generation. GLM-4.7 supports tool calling capabilities. Enable the tool call parser: ```shell Command python -m sglang.launch_server \ --model zai-org/GLM-4.7 \ --reasoning-parser glm45 \ --tool-call-parser glm47 \ --tp 8 \ --host 0.0.0.0 \ --port 8000 ``` **Python Example (with Thinking Process):** ```python Example from openai import OpenAI client = OpenAI( base_url="http://localhost:8000/v1", api_key="EMPTY" ) # Define available tools tools = [ { "type": "function", "function": { "name": "get_weather", "description": "Get the current weather for a location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city name" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "Temperature unit" } }, "required": ["location"] } } } ] # Make request with streaming to see thinking process response = client.chat.completions.create( model="zai-org/GLM-4.7", messages=[ {"role": "user", "content": "What's the weather in Beijing?"} ], tools=tools, temperature=0.7, stream=True ) # Process streaming response thinking_started = False has_thinking = False for chunk in response: if chunk.choices and len(chunk.choices) > 0: delta = chunk.choices[0].delta # Print thinking process if hasattr(delta, 'reasoning_content') and delta.reasoning_content: if not thinking_started: print("=============== Thinking =================", flush=True) thinking_started = True has_thinking = True print(delta.reasoning_content, end="", flush=True) # Print tool calls if hasattr(delta, 'tool_calls') and delta.tool_calls: # Close thinking section if needed if has_thinking and thinking_started: print("\n=============== Content =================", flush=True) thinking_started = False for tool_call in delta.tool_calls: if tool_call.function: print(f"Tool Call: {tool_call.function.name}") print(f" Arguments: {tool_call.function.arguments}") # Print content if delta.content: print(delta.content, end="", flush=True) print() ``` **Output Example:** ```text Output =============== Thinking ================= The user is asking about the weather in Beijing. I need to use the get_weather function to retrieve this information. I should call the function with location="Beijing". =============== Content ================= Tool Call: get_weather Arguments: {"location": "Beijing", "unit": "celsius"} ``` **Note:** - The reasoning parser shows how the model decides to use a tool - Tool calls are clearly marked with the function name and arguments - You can then execute the function and send the result back to continue the conversation **Handling Tool Call Results:** ```python Example # After getting the tool call, execute the function def get_weather(location, unit="celsius"): # Your actual weather API call here return f"The weather in {location} is 22°{unit[0].upper()} and sunny." # Send tool result back to the model messages = [ {"role": "user", "content": "What's the weather in Beijing?"}, { "role": "assistant", "content": None, "tool_calls": [{ "id": "call_123", "type": "function", "function": { "name": "get_weather", "arguments": '{"location": "Beijing", "unit": "celsius"}' } }] }, { "role": "tool", "tool_call_id": "call_123", "content": get_weather("Beijing", "celsius") } ] final_response = client.chat.completions.create( model="zai-org/GLM-4.7", messages=messages, temperature=0.7 ) print(final_response.choices[0].message.content) # Output: "The weather in Beijing is currently 22°C and sunny." ``` #### 4.2.3 Thinking Budget Limit the number of thinking tokens using `CustomLogitProcessor`. Launch with `--enable-custom-logit-processor`: ```python Example import openai from sglang.srt.sampling.custom_logit_processor import Glm4MoeThinkingBudgetLogitProcessor client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="*") response = client.chat.completions.create( model="zai-org/GLM-4.7", messages=[{"role": "user", "content": "Is Paris the Capital of France?"}], max_tokens=1024, extra_body={ "custom_logit_processor": Glm4MoeThinkingBudgetLogitProcessor().to_str(), "custom_params": {"thinking_budget": 512}, }, ) print(response) ``` ## 5. Benchmark This section uses **industry-standard configurations** for comparable benchmark results. ### 5.1 Speed Benchmark **Test Environment:** - Hardware: NVIDIA B200, NVIDIA GB200, AMD MI300X/MI325X/MI355X (8x) - Model: GLM-4.7-NVFP4 on NVIDIA Blackwell; GLM-4.7-FP8 or GLM-4.7 (BF16) on AMD - SGLang Version: 0.5.12 (NVIDIA Blackwell), 0.5.6.post1 (AMD) - Best per-GPU throughput config on B200: **TP=2 NVFP4 bf16-KV** (NVFP4 weights, no EP). Numbers below come from this config. **Benchmark Methodology:** We use industry-standard benchmark configurations to ensure results are comparable across frameworks and hardware platforms. #### 5.1.1 Standard Test Scenarios Four core scenarios reflect real-world usage patterns:

Scenario	Input Length	Output Length	Use Case
Chat	1K	1K	Most common conversational AI workload
Reasoning	1K	8K	Long-form generation, complex reasoning tasks
Summarization	8K	1K	Document summarization, RAG retrieval
Throughput	4K	1K	Mixed RAG / agent / multi-turn conversation (used for the inline B200 / GB200 results below)

#### 5.1.2 Concurrency Levels Test each scenario at three concurrency levels to capture the throughput vs. latency tradeoff (Pareto frontier): - **Low Concurrency**: `--max-concurrency 1` (Latency-optimized) - **Medium Concurrency**: `--max-concurrency 16` (Balanced) - **High Concurrency**: `--max-concurrency 100` (Throughput-optimized) — the Throughput (4K/1K) scenario uses `--max-concurrency 128` to match the inline B200/GB200 results below. #### 5.1.3 Number of Prompts For each concurrency level, configure `num_prompts` to simulate realistic user loads: - **Quick Test**: `num_prompts = concurrency × 1` (minimal test) - **Recommended**: `num_prompts = concurrency × 5` (standard benchmark) - **Stable Measurements**: `num_prompts = concurrency × 10` (production-grade) --- #### 5.1.4 Benchmark Commands **Scenario 1: Chat (1K/1K) - Most Important** - **Model Deployment** ```bash Command python -m sglang.launch_server \ --model zai-org/GLM-4.7 \ --tp 8 ``` - Low Concurrency (Latency-Optimized) ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model zai-org/GLM-4.7 \ --dataset-name random \ --random-input-len 1000 \ --random-output-len 1000 \ --num-prompts 10 \ --max-concurrency 1 \ --request-rate inf ``` - Medium Concurrency (Balanced) ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model zai-org/GLM-4.7 \ --dataset-name random \ --random-input-len 1000 \ --random-output-len 1000 \ --num-prompts 80 \ --max-concurrency 16 \ --request-rate inf ``` - High Concurrency (Throughput-Optimized) ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model zai-org/GLM-4.7 \ --dataset-name random \ --random-input-len 1000 \ --random-output-len 1000 \ --num-prompts 500 \ --max-concurrency 100 \ --request-rate inf ``` **Scenario 2: Reasoning (1K/8K)** - Low Concurrency ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model zai-org/GLM-4.7 \ --dataset-name random \ --random-input-len 1000 \ --random-output-len 8000 \ --num-prompts 10 \ --max-concurrency 1 \ --request-rate inf ``` - Medium Concurrency ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model zai-org/GLM-4.7 \ --dataset-name random \ --random-input-len 1000 \ --random-output-len 8000 \ --num-prompts 80 \ --max-concurrency 16 \ --request-rate inf ``` - High Concurrency ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model zai-org/GLM-4.7 \ --dataset-name random \ --random-input-len 1000 \ --random-output-len 8000 \ --num-prompts 320 \ --max-concurrency 64 \ --request-rate inf ``` **Scenario 3: Summarization (8K/1K)** - Low Concurrency ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model zai-org/GLM-4.7 \ --dataset-name random \ --random-input-len 8000 \ --random-output-len 1000 \ --num-prompts 10 \ --max-concurrency 1 \ --request-rate inf ``` - Medium Concurrency ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model zai-org/GLM-4.7 \ --dataset-name random \ --random-input-len 8000 \ --random-output-len 1000 \ --num-prompts 80 \ --max-concurrency 16 \ --request-rate inf ``` - High Concurrency ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model zai-org/GLM-4.7 \ --dataset-name random \ --random-input-len 8000 \ --random-output-len 1000 \ --num-prompts 320 \ --max-concurrency 64 \ --request-rate inf ``` **Scenario 4: Throughput (4K/1K) — NVIDIA Blackwell with NVFP4** The remaining sub-sections (§5.1.4.1 NVIDIA B200, §5.1.4.2 NVIDIA GB200) measure this scenario with `nvidia/GLM-4.7-NVFP4` weights and report the full `bench_serving` output verbatim. The same commands apply to other NVIDIA hardware after substituting the deployment line from §3.1. > **Note**: These runs use EOS-enabled generation (no `--disable-ignore-eos`), so generated-token counts reflect natural model behavior rather than a strict fixed-OSL pin. Compare against other EOS-enabled runs at the same workload, not against fixed-output-length benchmarks. #### 5.1.4.1 NVIDIA B200 **Model Deployment (NVIDIA B200, TP=2 NVFP4 — max tok/s/gpu config):** ```bash Command python -m sglang.launch_server \ --model nvidia/GLM-4.7-NVFP4 \ --tp-size 2 \ --mem-fraction-static 0.85 \ --reasoning-parser glm45 \ --tool-call-parser glm47 ``` - Low Concurrency (Latency-Optimized) ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model nvidia/GLM-4.7-NVFP4 \ --dataset-name random \ --random-input-len 4096 \ --random-output-len 1024 \ --num-prompts 5 \ --max-concurrency 1 \ --request-rate inf ``` ```text Output ============ Serving Benchmark Result ============ Backend: sglang Max request concurrency: 1 Successful requests: 5 Benchmark duration (s): 25.07 Total input tokens: 8105 Total generated tokens: 2674 Request throughput (req/s): 0.20 Input token throughput (tok/s): 323.25 Output token throughput (tok/s): 106.65 Total token throughput (tok/s): 429.90 Concurrency: 1.00 ----------------End-to-End Latency---------------- Mean E2E Latency (ms): 5011.93 Median E2E Latency (ms): 6441.44 ---------------Time to First Token---------------- Mean TTFT (ms): 179.61 Median TTFT (ms): 169.05 P99 TTFT (ms): 238.01 -----Time per Output Token (excl. 1st token)------ Mean TPOT (ms): 9.05 Median TPOT (ms): 9.03 P99 TPOT (ms): 9.16 ---------------Inter-Token Latency---------------- Mean ITL (ms): 9.05 Median ITL (ms): 9.05 ================================================== ``` - Medium Concurrency (Balanced) ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model nvidia/GLM-4.7-NVFP4 \ --dataset-name random \ --random-input-len 4096 \ --random-output-len 1024 \ --num-prompts 80 \ --max-concurrency 16 \ --request-rate inf ``` ```text Output ============ Serving Benchmark Result ============ Backend: sglang Max request concurrency: 16 Successful requests: 80 Benchmark duration (s): 60.60 Total input tokens: 179772 Total generated tokens: 39657 Request throughput (req/s): 1.32 Input token throughput (tok/s): 2966.39 Output token throughput (tok/s): 654.37 Total token throughput (tok/s): 3620.76 Concurrency: 14.01 ----------------End-to-End Latency---------------- Mean E2E Latency (ms): 10615.87 Median E2E Latency (ms): 9985.45 ---------------Time to First Token---------------- Mean TTFT (ms): 267.39 Median TTFT (ms): 177.26 P99 TTFT (ms): 584.29 -----Time per Output Token (excl. 1st token)------ Mean TPOT (ms): 20.98 Median TPOT (ms): 21.06 P99 TPOT (ms): 24.88 ---------------Inter-Token Latency---------------- Mean ITL (ms): 20.92 Median ITL (ms): 17.93 ================================================== ``` - High Concurrency (Throughput-Optimized) ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model nvidia/GLM-4.7-NVFP4 \ --dataset-name random \ --random-input-len 4096 \ --random-output-len 1024 \ --num-prompts 640 \ --max-concurrency 128 \ --request-rate inf ``` ```text Output ============ Serving Benchmark Result ============ Backend: sglang Max request concurrency: 128 Successful requests: 640 Benchmark duration (s): 172.95 Total input tokens: 1453591 Total generated tokens: 308740 Request throughput (req/s): 3.70 Input token throughput (tok/s): 8404.67 Output token throughput (tok/s): 1785.14 Total token throughput (tok/s): 10189.80 Concurrency: 117.85 ----------------End-to-End Latency---------------- Mean E2E Latency (ms): 31848.20 Median E2E Latency (ms): 28554.42 ---------------Time to First Token---------------- Mean TTFT (ms): 1598.40 Median TTFT (ms): 298.88 P99 TTFT (ms): 11015.96 -----Time per Output Token (excl. 1st token)------ Mean TPOT (ms): 65.94 Median TPOT (ms): 65.81 P99 TPOT (ms): 137.73 ---------------Inter-Token Latency---------------- Mean ITL (ms): 62.99 Median ITL (ms): 35.44 ================================================== ``` #### 5.1.4.2 NVIDIA GB200 **Model Deployment (NVIDIA GB200, TP=2 NVFP4 — max tok/s/gpu config):** ```bash Command python -m sglang.launch_server \ --model nvidia/GLM-4.7-NVFP4 \ --tp-size 2 \ --mem-fraction-static 0.85 \ --reasoning-parser glm45 \ --tool-call-parser glm47 ``` - Low Concurrency (Latency-Optimized) ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model nvidia/GLM-4.7-NVFP4 \ --dataset-name random \ --random-input-len 4096 \ --random-output-len 1024 \ --num-prompts 5 \ --max-concurrency 1 \ --request-rate inf ``` ```text Output ============ Serving Benchmark Result ============ Backend: sglang Max request concurrency: 1 Successful requests: 5 Benchmark duration (s): 24.74 Total input tokens: 8105 Total generated tokens: 2674 Request throughput (req/s): 0.20 Input token throughput (tok/s): 327.65 Output token throughput (tok/s): 108.10 Total token throughput (tok/s): 435.75 Concurrency: 1.00 ----------------End-to-End Latency---------------- Mean E2E Latency (ms): 4944.47 Median E2E Latency (ms): 6347.31 ---------------Time to First Token---------------- Mean TTFT (ms): 211.41 Median TTFT (ms): 207.25 P99 TTFT (ms): 226.46 -----Time per Output Token (excl. 1st token)------ Mean TPOT (ms): 8.86 Median TPOT (ms): 8.84 P99 TPOT (ms): 8.96 ---------------Inter-Token Latency---------------- Mean ITL (ms): 8.87 Median ITL (ms): 8.85 ================================================== ``` - Medium Concurrency (Balanced) ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model nvidia/GLM-4.7-NVFP4 \ --dataset-name random \ --random-input-len 4096 \ --random-output-len 1024 \ --num-prompts 80 \ --max-concurrency 16 \ --request-rate inf ``` ```text Output ============ Serving Benchmark Result ============ Backend: sglang Max request concurrency: 16 Successful requests: 80 Benchmark duration (s): 60.40 Total input tokens: 179772 Total generated tokens: 39657 Request throughput (req/s): 1.32 Input token throughput (tok/s): 2976.52 Output token throughput (tok/s): 656.61 Total token throughput (tok/s): 3633.13 Concurrency: 13.97 ----------------End-to-End Latency---------------- Mean E2E Latency (ms): 10611.51 Median E2E Latency (ms): 9956.84 ---------------Time to First Token---------------- Mean TTFT (ms): 338.14 Median TTFT (ms): 215.25 P99 TTFT (ms): 915.40 -----Time per Output Token (excl. 1st token)------ Mean TPOT (ms): 20.87 Median TPOT (ms): 21.36 P99 TPOT (ms): 27.05 ---------------Inter-Token Latency---------------- Mean ITL (ms): 20.77 Median ITL (ms): 16.53 ================================================== ``` - High Concurrency (Throughput-Optimized) ```bash Command python -m sglang.bench_serving \ --backend sglang \ --model nvidia/GLM-4.7-NVFP4 \ --dataset-name random \ --random-input-len 4096 \ --random-output-len 1024 \ --num-prompts 640 \ --max-concurrency 128 \ --request-rate inf ``` ```text Output ============ Serving Benchmark Result ============ Backend: sglang Max request concurrency: 128 Successful requests: 640 Benchmark duration (s): 181.89 Total input tokens: 1453591 Total generated tokens: 309221 Request throughput (req/s): 3.52 Input token throughput (tok/s): 7991.59 Output token throughput (tok/s): 1700.04 Total token throughput (tok/s): 9691.63 Concurrency: 118.86 ----------------End-to-End Latency---------------- Mean E2E Latency (ms): 33690.47 Median E2E Latency (ms): 30421.55 ---------------Time to First Token---------------- Mean TTFT (ms): 1353.16 Median TTFT (ms): 383.52 P99 TTFT (ms): 8940.53 -----Time per Output Token (excl. 1st token)------ Mean TPOT (ms): 69.88 Median TPOT (ms): 71.77 P99 TPOT (ms): 131.75 ---------------Inter-Token Latency---------------- Mean ITL (ms): 67.23 Median ITL (ms): 33.46 ================================================== ``` #### 5.1.5 Understanding the Results **Key Metrics:** - **Request Throughput (req/s)**: Number of requests processed per second - **Output Token Throughput (tok/s)**: Total tokens generated per second - **Mean TTFT (ms)**: Time to First Token - measures responsiveness - **Mean TPOT (ms)**: Time Per Output Token - measures generation speed - **Mean ITL (ms)**: Inter-Token Latency - measures streaming consistency **Why These Configurations Matter:** - **1K/1K (Chat)**: Represents the most common conversational AI workload. This is the highest priority scenario for most deployments. - **1K/8K (Reasoning)**: Tests long-form generation capabilities crucial for complex reasoning, code generation, and detailed explanations. - **8K/1K (Summarization)**: Evaluates performance with large context inputs, essential for RAG systems, document Q&A, and summarization tasks. - **4K/1K (Throughput)**: Realistic mixed workload typical of production deployments (RAG context + medium response). Long enough input that prefill matters, long enough output that decode steady-state dominates. Used for the inline B200 / GB200 results above. - **Variable Concurrency**: Captures the Pareto frontier - the optimal tradeoff between throughput and latency at different load levels. Low concurrency shows best-case latency, high concurrency shows maximum throughput. **Interpreting Results:** - Compare your results against baseline numbers for your hardware - Higher throughput at same latency = better performance - Lower TTFT = more responsive user experience - Lower TPOT = faster generation speed ### 5.2 Accuracy Benchmark Document model accuracy on standard benchmarks: #### 5.2.1 GSM8K Benchmark - Benchmark Command ```bash Command python -m sglang.test.few_shot_gsm8k \ --num-shots 5 \ --num-questions 1319 \ --port 30000 ``` - Test Result (NVIDIA B200, TP=2 NVFP4) ```text Output Accuracy: 0.946 Latency: 178.284 s Output throughput: 769.204 token/s ``` - Test Result (NVIDIA GB200, TP=2 NVFP4) ```text Output Accuracy: 0.951 Latency: 175.190 s Invalid: 0.000 ```