diff --git a/docs_new/cookbook/autoregressive/GLM/GLM-4.7.mdx b/docs_new/cookbook/autoregressive/GLM/GLM-4.7.mdx index 84b3418f9..5d568f2aa 100644 --- a/docs_new/cookbook/autoregressive/GLM/GLM-4.7.mdx +++ b/docs_new/cookbook/autoregressive/GLM/GLM-4.7.mdx @@ -1,14 +1,14 @@ --- title: GLM-4.7 metatags: - description: "Deploy GLM-4.7 with SGLang on AMD GPUs - state-of-the-art reasoning, enhanced coding, and robust tool calling capabilities." + 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 the latest and most powerful language model in the GLM series developed by Zhipu AI, featuring state-of-the-art capabilities in reasoning, function calling, and multi-modal understanding. +[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. -As the newest iteration in the GLM series, GLM-4.7 achieves significant improvements across all domains: +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 @@ -21,14 +21,15 @@ For more details, please refer to the [official GLM-4.7 documentation](https://d **Key Features:** - **State-of-the-Art Reasoning**: Enhanced reasoning capabilities for the most complex problem-solving tasks -- **Multiple Quantizations**: BF16 and FP8 variants for different performance/memory trade-offs -- **Hardware Optimization**: Specifically tuned for AMD MI300X/MI325X/MI355X GPUs +- **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) - Recommended for MI300X/MI325X/MI355X -- **FP8 (8-bit quantized)**: [zai-org/GLM-4.7-FP8](https://huggingface.co/zai-org/GLM-4.7-FP8) - Recommended for MI300X/MI325X/MI355X +- **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:** @@ -40,6 +41,36 @@ SGLang offers multiple installation methods. You can choose the most suitable in Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions. +**Docker Images by Hardware Platform:** + + + + + + + + + + + + + + + + + + + + + + + + + + +
Hardware PlatformDocker 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. @@ -54,9 +85,50 @@ import { GLM47Deployment } from "/src/snippets/autoregressive/glm-47-deployment. ### 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: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
HardwareNVFP4FP8BF16
B200 (8×, single node)tp=2 / 4 / 8tp=4 / 8tp=8
GB200 (NVL72, 4× per tray)tp=2 / 4tp=4—
H200 (8×)—tp=8tp=8
AMD MI300X / MI325X / MI355X—tp=2 / 4 / 8tp=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 (`SGLANG_ENABLE_SPEC_V2=True`); set `SGLANG_ENABLE_SPEC_V2=0` 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](../../../docs/basic_usage/glm45). Per-hardware bench commands and flags are inline in §5.1 below. + ## 4. Model Invocation ### 4.1 Basic Usage @@ -331,10 +403,10 @@ This section uses **industry-standard configurations** for comparable benchmark **Test Environment:** -- Hardware: AMD MI300X (8x), AMD MI325X (8x), AMD MI355X (8x) -- Model: GLM-4.7 -- Tensor Parallelism: 8 -- SGLang Version: 0.5.6.post1 +- 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:** @@ -342,7 +414,7 @@ We use industry-standard benchmark configurations to ensure results are comparab #### 5.1.1 Standard Test Scenarios -Three core scenarios reflect real-world usage patterns: +Four core scenarios reflect real-world usage patterns: @@ -378,6 +450,12 @@ Three core scenarios reflect real-world usage patterns: + + + + + +
1K Document summarization, RAG retrieval
**Throughput**4K1KMixed RAG / agent / multi-turn conversation (used for the inline B200 / GB200 results below)
@@ -387,7 +465,7 @@ Test each scenario at three concurrency levels to capture the throughput vs. lat - **Low Concurrency**: `--max-concurrency 1` (Latency-optimized) - **Medium Concurrency**: `--max-concurrency 16` (Balanced) -- **High Concurrency**: `--max-concurrency 100` (Throughput-optimized) +- **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 @@ -534,6 +612,302 @@ python -m sglang.bench_serving \ --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:** @@ -549,6 +923,7 @@ python -m sglang.bench_serving \ - **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:** @@ -567,6 +942,21 @@ Document model accuracy on standard benchmarks: - Benchmark Command ```bash Command python -m sglang.test.few_shot_gsm8k \ - --num-questions 200 \ + --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 +``` diff --git a/docs_new/src/snippets/autoregressive/glm-47-deployment.jsx b/docs_new/src/snippets/autoregressive/glm-47-deployment.jsx index 777265062..b80b90012 100644 --- a/docs_new/src/snippets/autoregressive/glm-47-deployment.jsx +++ b/docs_new/src/snippets/autoregressive/glm-47-deployment.jsx @@ -5,7 +5,10 @@ export const GLM47Deployment = () => { name: 'hardware', title: 'Hardware Platform', items: [ - { id: 'mi300x', label: 'MI300X', default: true }, + { id: 'b200', label: 'B200', default: true }, + { id: 'gb200', label: 'GB200', default: false }, + { id: 'h200', label: 'H200', default: false }, + { id: 'mi300x', label: 'MI300X', default: false }, { id: 'mi325x', label: 'MI325X', default: false }, { id: 'mi355x', label: 'MI355X', default: false } ] @@ -14,8 +17,18 @@ export const GLM47Deployment = () => { name: 'quantization', title: 'Quantization', items: [ - { id: 'bf16', label: 'BF16', default: true }, - { id: 'fp8', label: 'FP8', default: false } + { id: 'nvfp4', label: 'NVFP4', default: true }, + { id: 'fp8', label: 'FP8', default: false }, + { id: 'bf16', label: 'BF16', default: false } + ] + }, + gpus: { + name: 'gpus', + title: 'Number of GPUs', + items: [ + { id: '2', label: '2', default: false }, + { id: '4', label: '4', default: true }, + { id: '8', label: '8', default: false } ] }, strategy: { @@ -47,6 +60,20 @@ export const GLM47Deployment = () => { } }; + // §3.2 support matrix — single source of truth for the greyed-out controls and + // generateCommand. hardware -> weight type -> allowed TP sizes (missing key = unsupported). + const SUPPORT = { + b200: { nvfp4: [2, 4, 8], fp8: [4, 8], bf16: [8] }, + gb200: { nvfp4: [2, 4], fp8: [4] }, + h200: { fp8: [8], bf16: [8] }, + mi300x: { fp8: [2, 4, 8], bf16: [4, 8] }, + mi325x: { fp8: [2, 4, 8], bf16: [4, 8] }, + mi355x: { fp8: [2, 4, 8], bf16: [4, 8] }, + }; + const quantSupported = (hw, q) => Boolean(SUPPORT[hw] && SUPPORT[hw][q]); + const allowedTps = (hw, q) => (SUPPORT[hw] && SUPPORT[hw][q]) || []; + const firstSupportedQuant = (hw) => Object.keys(SUPPORT[hw] || {})[0] || 'fp8'; + // Initialize state const getInitialState = () => { const initialState = {}; @@ -80,7 +107,21 @@ export const GLM47Deployment = () => { }, []); const handleRadioChange = (optionName, value) => { - setValues(prev => ({ ...prev, [optionName]: value })); + setValues(prev => { + const next = { ...prev, [optionName]: value }; + // Keep weight type + GPU count within the §3.2 matrix as hardware/quant change, + // so the displayed command is always a supported configuration. + if (optionName === 'hardware' || optionName === 'quantization') { + if (!quantSupported(next.hardware, next.quantization)) { + next.quantization = firstSupportedQuant(next.hardware); + } + const tps = allowedTps(next.hardware, next.quantization); + if (tps.length && !tps.includes(parseInt(next.gpus, 10))) { + next.gpus = String(tps.includes(4) ? 4 : tps[0]); + } + } + return next; + }); }; const handleCheckboxChange = (optionName, itemId, isChecked) => { @@ -96,47 +137,81 @@ export const GLM47Deployment = () => { // Generate command const generateCommand = () => { - const { hardware, quantization, strategy, thinking, toolcall } = values; + const { hardware, quantization, gpus, strategy, thinking, toolcall } = values; const strategyArray = Array.isArray(strategy) ? strategy : []; - const modelSuffix = quantization === 'fp8' ? '-FP8' : ''; - const modelName = `zai-org/GLM-4.7${modelSuffix}`; + const isNvidiaBlackwell = hardware === 'b200' || hardware === 'gb200'; + const isAMD = hardware === 'mi300x' || hardware === 'mi325x' || hardware === 'mi355x'; - // Determine TP value based on hardware and quantization - let tpValue = 4; // Default for MI300X and MI325X - if (hardware === 'mi355x') { - tpValue = quantization === 'fp8' ? 2 : 4; // MI355X: TP=2 for FP8, TP=4 for BF16 + // Only emit §3.2-supported commands; guards any stale (greyed-out) selection. + if (!quantSupported(hardware, quantization)) { + return ( + `# ${quantization.toUpperCase()} is not supported on ${hardware.toUpperCase()} per the §3.2 matrix.\n` + + `# Pick a highlighted weight type above.` + ); + } + + // Pick model checkpoint by weight type + let modelName = 'zai-org/GLM-4.7'; + if (quantization === 'nvfp4') { + modelName = 'nvidia/GLM-4.7-NVFP4'; + } else if (quantization === 'fp8') { + modelName = 'zai-org/GLM-4.7-FP8'; } let cmd = 'python -m sglang.launch_server \\\n'; cmd += ` --model ${modelName}`; - // TP is mandatory - cmd += ` \\\n --tp ${tpValue}`; + if (isAMD) { + // AMD (MI300X / MI325X / MI355X): validated pre-Blackwell command shape. + // TP is fixed per chip + weight type, so the GPU-count selector is unused here. + let tpValue = 4; // MI300X / MI325X default + if (hardware === 'mi355x') { + tpValue = quantization === 'fp8' ? 2 : 4; // MI355X: TP=2 FP8, TP=4 BF16 + } + cmd += ` \\\n --tp ${tpValue}`; - // MI300X/MI325X BF16 requires extra flags - if ((hardware === 'mi300x' || hardware === 'mi325x') && quantization === 'bf16') { - cmd += ` \\\n --max-context-length 8192 \\\n --mem-fraction-static 0.9`; + // MI300X/MI325X BF16 requires extra flags + if ((hardware === 'mi300x' || hardware === 'mi325x') && quantization === 'bf16') { + cmd += ` \\\n --max-context-length 8192 \\\n --mem-fraction-static 0.9`; + } + if (strategyArray.includes('dp')) { + cmd += ` \\\n --dp 8 \\\n --enable-dp-attention`; + } + if (strategyArray.includes('ep')) { + cmd += ` \\\n --ep 8`; + } + } else { + // NVIDIA (B200 / GB200 / H200): TP follows the "Number of GPUs" selector, + // clamped to a §3.2-supported value for the chosen hardware + weight type. + const tps = allowedTps(hardware, quantization); + let tpValue = parseInt(gpus, 10) || tps[0]; + if (!tps.includes(tpValue)) { + tpValue = tps.includes(4) ? 4 : tps[0]; + } + cmd += ` \\\n --tp-size ${tpValue}`; + + // Blackwell + NVFP4: enable EP when the user selected it + if (isNvidiaBlackwell && quantization === 'nvfp4' && strategyArray.includes('ep')) { + cmd += ` \\\n --ep ${tpValue}`; + } + // Blackwell + NVFP4: leave headroom for cuda-graph capture + if (isNvidiaBlackwell && quantization === 'nvfp4') { + cmd += ` \\\n --mem-fraction-static 0.85`; + } } - // Strategy-specific parameters - if (strategyArray.includes('dp')) { - cmd += ` \\\n --dp 8 \\\n --enable-dp-attention`; - } - if (strategyArray.includes('ep')) { - cmd += ` \\\n --ep 8`; - } + // MTP / EAGLE speculative decoding (all platforms) if (strategyArray.includes('mtp')) { cmd = 'SGLANG_ENABLE_SPEC_V2=1 ' + cmd; cmd += ` \\\n --speculative-algorithm EAGLE \\\n --speculative-num-steps 3 \\\n --speculative-eagle-topk 1 \\\n --speculative-num-draft-tokens 4`; } - // Add tool call parser if enabled if (toolcall === 'enabled') { cmd += ` \\\n --tool-call-parser glm47`; } - // Add thinking parser if enabled + // glm45 is the registered reasoning detector; glm47 is only valid for tool-call. if (thinking === 'enabled') { cmd += ` \\\n --reasoning-parser glm45`; } @@ -155,16 +230,31 @@ export const GLM47Deployment = () => { const subtitleStyle = { display: 'block', fontSize: '9px', marginTop: '1px', lineHeight: '1.1', opacity: 0.7 }; const commandDisplayStyle = { flex: 1, padding: '12px 16px', background: isDark ? '#111827' : '#f5f5f5', borderRadius: '6px', fontFamily: "'Menlo', 'Monaco', 'Courier New', monospace", fontSize: '12px', lineHeight: '1.5', color: isDark ? '#e5e7eb' : '#374151', whiteSpace: 'pre-wrap', overflowX: 'auto', margin: 0, border: `1px solid ${isDark ? '#374151' : '#e5e7eb'}` }; + // Which Deployment Strategy toggles apply (mirrors generateCommand): DP only on + // AMD; EP only on AMD or Blackwell + NVFP4 — greyed otherwise. + const hwSel = values.hardware; + const isAMDSel = hwSel === 'mi300x' || hwSel === 'mi325x' || hwSel === 'mi355x'; + const isBlackwellSel = hwSel === 'b200' || hwSel === 'gb200'; + const strategyApplies = (id) => { + if (id === 'dp') return isAMDSel; + if (id === 'ep') return isAMDSel || (isBlackwellSel && values.quantization === 'nvfp4'); + return true; // tp (required) and mtp (all platforms) + }; + return (
- {Object.entries(options).map(([key, option]) => ( + {Object.entries(options).map(([key, option]) => { + // GPU count is fixed (greyed) on AMD; on NVIDIA individual counts are greyed + // per the §3.2 matrix. Weight types unsupported on the hardware are greyed too. + const gpusGroupAMD = key === 'gpus' && isAMDSel; + return (
-
{option.title}
+
{option.title}{gpusGroupAMD ? ' (N/A for AMD)' : ''}
{option.type === 'checkbox' ? ( option.items.map(item => { const isChecked = (values[option.name] || []).includes(item.id); - const isDisabled = item.required; + const isDisabled = item.required || (key === 'strategy' && !strategyApplies(item.id)); return (
- ))} + ); + })}
Run this Command:
{generateCommand()}