Can DeepSeek LLM 67B run on B100 192GB?

YES — Runs Great

B59Good

Estimated from fit model

DeepSeek LLM 67B needs ~66.8 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~179 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 66.8 GB, 178.8 tok/s, Runs well

66.8 GB required192.0 GB available

35% VRAM used

Fit status

Runs well

Decode

178.8 tok/s

TTFT

1083 ms

Safe context

Memory

66.8 GB / 192.0 GB

Memory breakdown

Weights40.9 GB

KV Cache5.8 GB

Runtime0.9 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsDeepSeek LLM 67B on B100 192GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 178.8 tok/s decode · 1.1s TTFT (warm) · 447 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	178.8 tok/s	591 ms	4K
Coding	B	Runs well	178.8 tok/s	1083 ms	4K
Agentic Coding	B	Runs well	178.8 tok/s	1575 ms	4K
Reasoning	B	Runs well	178.8 tok/s	1280 ms	4K
RAG	B	Runs well	178.8 tok/s	1969 ms	4K

Quantization options

How DeepSeek LLM 67B (67B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	26.1 GB	Low	C48
Q3_K_S	3	32.8 GB	Low	C49
NVFP4	4	37.5 GB	Medium	C50
Q4_K_M	4	40.9 GB	Medium	C50
Q5_K_M	5	48.2 GB	High	C51
Q6_K	6	54.9 GB	High	C51
Q8_0	8	71.7 GB	Very High	C53
F16Best for your GPU	16	137.4 GB	Maximum	B58

Get started

Copy-paste commands to run DeepSeek LLM 67B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "deepseek-ai/deepseek-llm-67b-chat" \
  --hf-file "deepseek-llm-67b-chat-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can B100 192GB run DeepSeek LLM 67B?

Yes, B100 192GB can run DeepSeek LLM 67B with a B grade (Runs well). Expected decode speed: 178.8 tok/s.

How much VRAM does DeepSeek LLM 67B need?

DeepSeek LLM 67B (67B parameters) requires approximately 66.8 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek LLM 67B?

The recommended quantization for DeepSeek LLM 67B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek LLM 67B run at on B100 192GB?

On B100 192GB, DeepSeek LLM 67B achieves approximately 178.8 tokens per second decode speed with a time-to-first-token of 1083ms using Q4_K_M quantization.

Can B100 192GB run DeepSeek LLM 67B for coding?

For coding workloads, DeepSeek LLM 67B on B100 192GB receives a B grade with 178.8 tok/s and 4K context.

What context window can DeepSeek LLM 67B use on B100 192GB?

On B100 192GB, DeepSeek LLM 67B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for B100 192GB See all hardware for DeepSeek LLM 67B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

178.8 tok/s

591 ms

Coding

Runs well

178.8 tok/s

1083 ms

Agentic Coding

Runs well

178.8 tok/s

1575 ms

Reasoning

Runs well

178.8 tok/s

1280 ms

RAG

Runs well

178.8 tok/s

1969 ms

Quantization options

How DeepSeek LLM 67B (67B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	26.1 GB	Low	C48
Q3_K_S	3	32.8 GB	Low	C49
NVFP4	4	37.5 GB	Medium	C50
Q4_K_M	4	40.9 GB	Medium	C50
Q5_K_M	5	48.2 GB	High	C51
Q6_K	6	54.9 GB	High	C51
Q8_0	8	71.7 GB	Very High	C53
F16Best for your GPU	16	137.4 GB	Maximum	B58

Frequently asked questions

Can B100 192GB run DeepSeek LLM 67B?

Yes, B100 192GB can run DeepSeek LLM 67B with a B grade (Runs well). Expected decode speed: 178.8 tok/s.

How much VRAM does DeepSeek LLM 67B need?

DeepSeek LLM 67B (67B parameters) requires approximately 66.8 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek LLM 67B?

The recommended quantization for DeepSeek LLM 67B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek LLM 67B run at on B100 192GB?

On B100 192GB, DeepSeek LLM 67B achieves approximately 178.8 tokens per second decode speed with a time-to-first-token of 1083ms using Q4_K_M quantization.

Can B100 192GB run DeepSeek LLM 67B for coding?

For coding workloads, DeepSeek LLM 67B on B100 192GB receives a B grade with 178.8 tok/s and 4K context.

What context window can DeepSeek LLM 67B use on B100 192GB?

On B100 192GB, DeepSeek LLM 67B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.