Can Llama 3.3 70B run on B100 192GB?

YES — Runs Great

A84Great

Estimated from fit model

Llama 3.3 70B needs ~68.0 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~171 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 68.0 GB, 171.1 tok/s, Runs well

68.0 GB required192.0 GB available

35% VRAM used

Fit status

Runs well

Decode

171.1 tok/s

TTFT

1131 ms

Safe context

128K

Memory

68.0 GB / 192.0 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B 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: 171.1 tok/s decode · 1.1s TTFT (warm) · 428 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	A	Runs well	171.1 tok/s	617 ms	128K
Coding	A	Runs well	171.1 tok/s	1131 ms	128K
Agentic Coding	A	Runs well	171.1 tok/s	1645 ms	128K
Reasoning	A	Runs well	171.1 tok/s	1337 ms	128K
RAG	A	Runs well	171.1 tok/s	2057 ms	128K

Quantization options

How Llama 3.3 70B (70B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A73
Q3_K_S	3	34.3 GB	Low	A74
NVFP4	4	39.2 GB	Medium	A74
Q4_K_M	4	42.7 GB	Medium	A75
Q5_K_M	5	50.4 GB	High	A75
Q6_K	6	57.4 GB	High	A76
Q8_0	8	74.9 GB	Very High	A78
F16Best for your GPU	16	143.5 GB	Maximum	A82

Get started

Copy-paste commands to run Llama 3.3 70B on your machine.

Run

ollama run llama3.3

Your hardware

More models your B100 192GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	97.4 tok/s
Qwen 3.5 122B A10B	122B	S	270.2 tok/s
DeepSeek V4 Flash	284B	S	144.8 tok/s
Mistral Small 4 119B	119B	S	292.9 tok/s
GPT-OSS 120B	117B	S	102.4 tok/s

Frequently asked questions

Can B100 192GB run Llama 3.3 70B?

Yes, B100 192GB can run Llama 3.3 70B with a A grade (Runs well). Expected decode speed: 171.1 tok/s.

How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 68.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B?

The recommended quantization for Llama 3.3 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.3 70B run at on B100 192GB?

On B100 192GB, Llama 3.3 70B achieves approximately 171.1 tokens per second decode speed with a time-to-first-token of 1131ms using Q4_K_M quantization.

Can B100 192GB run Llama 3.3 70B for coding?

For coding workloads, Llama 3.3 70B on B100 192GB receives a A grade with 171.1 tok/s and 128K context.

What context window can Llama 3.3 70B use on B100 192GB?

On B100 192GB, Llama 3.3 70B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for B100 192GB See all hardware for Llama 3.3 70B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

171.1 tok/s

617 ms

128K

Coding

Runs well

171.1 tok/s

1131 ms

128K

Agentic Coding

Runs well

171.1 tok/s

1645 ms

128K

Reasoning

Runs well

171.1 tok/s

1337 ms

128K

RAG

Runs well

171.1 tok/s

2057 ms

128K

Quantization options

How Llama 3.3 70B (70B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A73
Q3_K_S	3	34.3 GB	Low	A74
NVFP4	4	39.2 GB	Medium	A74
Q4_K_M	4	42.7 GB	Medium	A75
Q5_K_M	5	50.4 GB	High	A75
Q6_K	6	57.4 GB	High	A76
Q8_0	8	74.9 GB	Very High	A78
F16Best for your GPU	16	143.5 GB	Maximum	A82

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

97.4 tok/s

Qwen 3.5 122B A10B

122B

270.2 tok/s

DeepSeek V4 Flash

284B

144.8 tok/s

Mistral Small 4 119B

119B

292.9 tok/s

GPT-OSS 120B

117B

102.4 tok/s

Frequently asked questions

Can B100 192GB run Llama 3.3 70B?

Yes, B100 192GB can run Llama 3.3 70B with a A grade (Runs well). Expected decode speed: 171.1 tok/s.

How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 68.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B?

The recommended quantization for Llama 3.3 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.3 70B run at on B100 192GB?

On B100 192GB, Llama 3.3 70B achieves approximately 171.1 tokens per second decode speed with a time-to-first-token of 1131ms using Q4_K_M quantization.

Can B100 192GB run Llama 3.3 70B for coding?

For coding workloads, Llama 3.3 70B on B100 192GB receives a A grade with 171.1 tok/s and 128K context.

What context window can Llama 3.3 70B use on B100 192GB?

On B100 192GB, Llama 3.3 70B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.