DeepSeek-Math-V2
1. Model Introduction
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 - Full precision weights
License: To use DeepSeek-Math-V2, you must agree to DeepSeek's Community License. See LICENSE for details.
2. SGLang Installation
Please refer to the official SGLang installation guide 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.
Hardware Platform
Reasoning Parser
DP Attention
Run this 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
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
--dpvalue commonly matches the--tpvalue - 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:
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:
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:
=============== 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:
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:
=============== 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:
python3 benchmark/gsm8k/bench_sglang.py --num-questions 200 --port 30000
Test Results:
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:
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:
============ 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:
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:
============ 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
==================================================