---
title: DeepSeek-Math-V2
metatags:
description: "Deploy DeepSeek-Math-V2 with SGLang - advanced mathematical reasoning model with gold-level IMO/CMO performance and theorem-proving capabilities."
---
import { DeepSeekMathV2Deployment } from '/src/snippets/autoregressive/deepseek-math-v2-deployment.jsx';
## 1. Model Introduction
[DeepSeek-Math-V2](https://huggingface.co/deepseek-ai/DeepSeek-Math-V2) is DeepSeek's advanced mathematical reasoning model with strong theorem-proving capabilities. The model demonstrates exceptional performance on mathematical competitions, achieving gold-level scores on IMO 2025 and CMO 2024, and a near-perfect 118/120 on Putnam 2024 with scaled test-time compute.
**Key Features:**
- **Strong Theorem-Proving**: Gold-level performance on IMO 2025 and CMO 2024
- **Self-Verifiable Reasoning**: Implements self-verifiable mathematical reasoning for improved accuracy
- **Competition-Level Math**: Near-perfect score (118/120) on Putnam 2024
- **Large MoE Model**: ~671B total parameters, requires high-memory GPUs (B200 183GB or B300 275GB)
**Available Models:**
- **BF16 (Full Weights)**: [deepseek-ai/DeepSeek-Math-V2](https://huggingface.co/deepseek-ai/DeepSeek-Math-V2) - Full precision weights
**License:**
To use DeepSeek-Math-V2, you must agree to DeepSeek's Community License. See [LICENSE](https://huggingface.co/deepseek-ai/DeepSeek-Math-V2/blob/main/LICENSE) for details.
## 2. SGLang Installation
Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.
## 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, and deployment strategy.
DeepSeek-Math-V2 is built on DeepSeek-V3.2 and uses DSA sparse attention. All recipes here run the DSA indexer top-k on the default `--dsa-topk-backend sgl-kernel`. Other top-k backend choices have not been fully validated on this model.
### 3.2 Configuration Tips
**Hardware Requirements:**
- **B200 (183GB)**: BF16 tp=8
- **B300 (275GB)**: BF16 tp=8
**DP Attention:**
- Enable DP attention for high-throughput scenarios
- The `--dp` value commonly matches the `--tp` value
- Trade-off: Higher throughput at the cost of slightly increased latency
## 4. Model Invocation
### 4.1 Deployment Command
Deploy the model using the command generated above. Example for B200:
```shell Command
sglang serve --model-path deepseek-ai/DeepSeek-Math-V2 \
--tp 8 \
--ep 8 \
--reasoning-parser deepseek-r1 \
--host 0.0.0.0 \
--port 30000
```
### 4.2 Mathematical Reasoning
DeepSeek-Math-V2 excels at mathematical problem-solving with step-by-step reasoning.
**Streaming with Thinking Process:**
```python Example
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:30000/v1",
api_key="EMPTY"
)
# Mathematical reasoning problem
response = client.chat.completions.create(
model="deepseek-ai/DeepSeek-Math-V2",
messages=[
{"role": "user", "content": "Prove that for any positive integer n, the sum 1 + 2 + 3 + ... + n = n(n+1)/2"}
],
max_tokens=4096,
stream=True
)
# Process the stream
thinking_started = False
has_thinking = False
has_answer = 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:
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 =================
We need to prove that for any positive integer n, the sum 1 + 2 + 3 + ... + n = n(n+1)/2.
This is a classic formula for the sum of the first n natural numbers. We can prove by induction.
Base case: n=1, LHS = 1, RHS = 1*(1+1)/2 = 1*2/2 = 1. Holds.
Inductive step: Assume true for n = k, i.e., 1 + 2 + ... + k = k(k+1)/2. Then for n = k+1, sum = 1 + 2 + ... + k + (k+1) = [k(k+1)/2] + (k+1) = (k(k+1) + 2(k+1))/2 = (k+1)(k+2)/2 = (k+1
)((k+1)+1)/2. So holds for k+1. By induction, holds for all positive integers n.
...
=============== Content =================
We can prove the well-known formula for the sum of the first \(n\) positive integers in several ways. Two of the most elementary are presented below.
---
### 1. Proof by mathematical induction
**Base case (\(n=1\))**:
\[
1 = \frac{1\cdot(1+1)}{2}= \frac{1\cdot2}{2}=1,
\]
so the formula holds for \(n=1\).
**Inductive hypothesis:**
Assume that for some positive integer \(k\) the formula is true, i.e.
\[
1+2+\dots+k = \frac{k(k+1)}{2}.
\]
**Inductive step (\(k \to k+1\))**:
Consider the sum up to \(k+1\):
\[
\begin{aligned}
1+2+\dots+k+(k+1) &= \bigl(1+2+\dots+k\bigr) + (k+1) \\[4pt]
&= \frac{k(k+1)}{2} + (k+1) \qquad\text{(by the induction hypothesis)}\\[4pt]
&= (k+1)\left(\frac{k}{2}+1\right)\\[4pt]
&= (k+1)\frac{k+2}{2}\\[4pt]
&= \frac{(k+1)(k+2)}{2}\\[4pt]
&= \frac{(k+1)\bigl((k+1)+1\bigr)}{2}.
\end{aligned}
\]
Thus the formula also holds for \(n=k+1\).
By the principle of mathematical induction,
\[
1+2+3+\dots+n = \frac{n(n+1)}{2}
\]
for every positive integer \(n\).
---
### 2. Proof by pairing (Gauss’s trick)
Let
\[
S = 1 + 2 + 3 + \dots + n.
\]
Write the same sum in reverse order:
\[
S = n + (n-1) + (n-2) + \dots + 1.
\]
Add the two equalities term‑by‑term:
\[
\begin{aligned}
2S &= (1+n) + \bigl(2+(n-1)\bigr) + \bigl(3+(n-2)\bigr) + \dots + (n+1)\\
&= \underbrace{(n+1)+(n+1)+\dots+(n+1)}_{n\ \text{times}}\\
&= n\,(n+1).
\end{aligned}
\]
Therefore
\[
S = \frac{n(n+1)}{2}.
\]
Both proofs are rigorous and show that the formula holds for all positive integers \(n\).
```
### 4.3 Competition-Level Problems
**Example: IMO-style Problem:**
```python Example
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:30000/v1",
api_key="EMPTY"
)
# IMO-style problem
response = client.chat.completions.create(
model="deepseek-ai/DeepSeek-Math-V2",
messages=[
{"role": "user", "content": "Let a, b, c be positive real numbers such that abc = 1. Prove that (a-1+1/b)(b-1+1/c)(c-1+1/a) <= 1."}
],
max_tokens=8192,
stream=True
)
# Process the stream
thinking_started = False
has_thinking = False
has_answer = False
for chunk in response:
if chunk.choices and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
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)
if delta.content:
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 =================
We need to prove that for positive real numbers a,b,c with abc = 1, we have:
\[
(a - 1 + \frac{1}{b})(b - 1 + \frac{1}{c})(c - 1 + \frac{1}{a}) \le 1.
\]
We can rewrite the expressions: Since abc=1, we have 1/b = ac, 1/c = ab, 1/a = bc. Wait careful: abc=1 => 1/b = ac? Actually 1/b = ac? Let's check: abc=1 => ac = 1/b? Multiply both sides by something: abc=1 => (ac) b = 1 => ac = 1/b. Yes, because (ac) * b = 1 => ac = 1/b. Similarly, ab = 1/c, bc = 1/a. So we can rewrite:
...
=============== Content =================
We are given positive real numbers \(a,b,c\) with \(abc=1\). We must prove
\[
\Bigl(a-1+\frac1b\Bigr)\Bigl(b-1+\frac1c\Bigr)\Bigl(c-1+\frac1a\Bigr)\le 1 .
\]
---
### 1. A convenient substitution
Because \(abc=1\), we can write
\[
a=\frac{x}{y},\qquad b=\frac{y}{z},\qquad c=\frac{z}{x}
\]
with positive numbers \(x,y,z\).
(For instance, take \(x=1,\;y=\frac1a,\;z=\frac1{ab}\); then indeed \(a=\frac{x}{y},\;b=\frac{y}{z}\) and, using \(abc=1\), we obtain \(c=\frac{z}{x}=\frac1{ab}=c\).)
---
### 2. Rewriting the factors
\[
\begin{aligned}
a-1+\frac1b &=\frac{x}{y}-1+\frac{z}{y}= \frac{x+z-y}{y},\\[2mm]
b-1+\frac1c &=\frac{y}{z}-1+\frac{x}{z}= \frac{x+y-z}{z},\\[2mm]
c-1+\frac1a &=\frac{z}{x}-1+\frac{y}{x}= \frac{y+z-x}{x}.
\end{aligned}
\]
Hence the product becomes
\[
P=\Bigl(a-1+\frac1b\Bigr)\Bigl(b-1+\frac1c\Bigr)\Bigl(c-1+\frac1a\Bigr)
=\frac{(x+z-y)(x+y-z)(y+z-x)}{xyz}.
\]
---
### 3. Reducing to a known inequality
We have to show \(P\le1\), i.e.
\[
(x+z-y)(x+y-z)(y+z-x)\le xyz .
\tag{1}
\]
Set
\[
p=x+y+z,\qquad q=xy+yz+zx,\qquad r=xyz .
\]
Notice that
\[
x+z-y=p-2y,\quad x+y-z=p-2z,\quad y+z-x=p-2x .
\]
Therefore
\[
\begin{aligned}
(x+z-y)(x+y-z)(y+z-x)
&=(p-2x)(p-2y)(p-2z)\\
&=p^{3}-2p^{2}(x+y+z)+4p(xy+yz+zx)-8xyz\\
&=-p^{3}+4pq-8r .
\end{aligned}
\]
Inequality (1) is thus equivalent to
\[
-p^{3}+4pq-8r\le r\quad\Longleftrightarrow\quad 4pq-p^{3}\le 9r .
\tag{2}
\]
---
### 4. Applying Schur’s inequality
Schur’s inequality of third degree states that for any non‑negative \(x,y,z\)
\[
p^{3}+9r\ge 4pq .
\]
Rearranged, this is exactly \(4pq-p^{3}\le 9r\), which is (2).
Since our \(x,y,z\) are positive, Schur’s inequality applies and (2) holds.
Consequently (1) is true, and we obtain \(P\le1\).
---
### 5. Equality case
Equality in Schur’s inequality for positive numbers occurs only when \(x=y=z\).
Then \(a=b=c=1\), and indeed the product equals \(1\).
---
Thus for all positive \(a,b,c\) with \(abc=1\),
\[
\Bigl(a-1+\frac1b\Bigr)\Bigl(b-1+\frac1c\Bigr)\Bigl(c-1+\frac1a\Bigr)\le 1 .
\]
∎
```
## 5. Benchmark
### 5.1 Accuracy Benchmark
#### 5.1.1 GSM8K Benchmark
**Benchmark Command:**
```shell Command
python3 benchmark/gsm8k/bench_sglang.py --num-questions 200 --port 30000
```
**Test Results:**
```text Output
Accuracy: 0.975
Invalid: 0.000
Latency: 34.358 s
Output throughput: 540.162 token/s
```
### 5.2 Speed Benchmark
**Test Environment:**
- Hardware: NVIDIA B200 GPU (8x, 183GB each)
- Model: DeepSeek-Math-V2
- Tensor Parallelism: 8
- SGLang Version: 0.5.8
#### 5.2.1 Latency Benchmark
**Benchmark Command:**
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--host 127.0.0.1 \
--port 30000 \
--model deepseek-ai/DeepSeek-Math-V2 \
--random-input-len 1024 \
--random-output-len 1024 \
--num-prompts 10 \
--max-concurrency 1
```
**Test Results:**
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 1
Successful requests: 10
Benchmark duration (s): 53.34
Total input tokens: 1972
Total input text tokens: 1972
Total generated tokens: 2784
Total generated tokens (retokenized): 2778
Request throughput (req/s): 0.19
Input token throughput (tok/s): 36.97
Output token throughput (tok/s): 52.19
Peak output token throughput (tok/s): 56.00
Peak concurrent requests: 3
Total token throughput (tok/s): 89.16
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 5330.72
Median E2E Latency (ms): 5879.28
P90 E2E Latency (ms): 8320.33
P99 E2E Latency (ms): 9921.29
---------------Time to First Token----------------
Mean TTFT (ms): 183.38
Median TTFT (ms): 177.92
P99 TTFT (ms): 217.64
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 17.96
Median TPOT (ms): 18.39
P99 TPOT (ms): 19.03
---------------Inter-Token Latency----------------
Mean ITL (ms): 18.57
Median ITL (ms): 18.63
P95 ITL (ms): 19.26
P99 ITL (ms): 19.48
Max ITL (ms): 24.93
==================================================
```
#### 5.2.2 Throughput Benchmark
**Benchmark Command:**
```shell Command
python3 -m sglang.bench_serving \
--backend sglang \
--host 127.0.0.1 \
--port 30000 \
--model deepseek-ai/DeepSeek-Math-V2 \
--random-input-len 1024 \
--random-output-len 1024 \
--num-prompts 1000 \
--max-concurrency 100
```
**Test Results:**
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 100
Successful requests: 1000
Benchmark duration (s): 217.36
Total input tokens: 301701
Total input text tokens: 301701
Total generated tokens: 188375
Total generated tokens (retokenized): 187456
Request throughput (req/s): 4.60
Input token throughput (tok/s): 1388.05
Output token throughput (tok/s): 866.67
Peak output token throughput (tok/s): 2589.00
Peak concurrent requests: 109
Total token throughput (tok/s): 2254.72
Concurrency: 89.81
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 19521.73
Median E2E Latency (ms): 12076.76
P90 E2E Latency (ms): 47248.87
P99 E2E Latency (ms): 86862.79
---------------Time to First Token----------------
Mean TTFT (ms): 790.40
Median TTFT (ms): 456.81
P99 TTFT (ms): 4223.33
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 106.52
Median TPOT (ms): 107.24
P99 TPOT (ms): 238.33
---------------Inter-Token Latency----------------
Mean ITL (ms): 100.29
Median ITL (ms): 38.34
P95 ITL (ms): 237.00
P99 ITL (ms): 347.49
Max ITL (ms): 3642.56
==================================================
```