Can Llama 3.2 11B Vision run on B100 192GB?

YES — Runs Great

B59Good

Estimated from fit model

Llama 3.2 11B Vision needs ~29.1 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~154 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) — 29.1 GB, 154.0 tok/s, Runs well

29.1 GB required192.0 GB available

15% VRAM used

Fit status

Runs well

Decode

154.0 tok/s

TTFT

1257 ms

Safe context

16K

Memory

29.1 GB / 192.0 GB

Memory breakdown

Weights6.7 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsLlama 3.2 11B Vision on B100 192GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 154.0 tok/s decode · 1.3s TTFT (warm) · 385 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	154.0 tok/s	686 ms	16K
Coding	B	Runs well	154.0 tok/s	1257 ms	16K
Agentic Coding	B	Runs well	154.0 tok/s	1829 ms	16K
Reasoning	B	Runs well	154.0 tok/s	1486 ms	16K
RAG	B	Runs well	154.0 tok/s	2286 ms	16K

Inference speed

Llama 3.2 11B Vision inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Llama 3.2 11B Vision at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~154 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	154.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	122.7	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	110.7	Fits
RTX 3090 24GB	24 GB	Q4_K_M	105.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	97.9	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	89.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	74.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	70.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	60.6	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	48.5	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	48.5	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	38.5	Fits
RTX 3060 12GB	12 GB	Q4_K_M	38.1	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	35.2	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	29.6	Fits
RTX 4060 8GB	8 GB	Q4_K_M	13.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.3 GB	Low	C51
Q3_K_S	3	5.4 GB	Low	C51
NVFP4	4	6.2 GB	Medium	C51
Q4_K_M	4	6.7 GB	Medium	C51
Q5_K_M	5	7.9 GB	High	C51
Q6_K	6	9.0 GB	High	C51
Q8_0	8	11.8 GB	Very High	C51
F16Best for your GPU	16	22.5 GB	Maximum	C52

Get started

Copy-paste commands to run Llama 3.2 11B Vision on your machine.

Run

ollama run llama3.2-vision:11b

Frequently asked questions

Can B100 192GB run Llama 3.2 11B Vision?

Yes, B100 192GB can run Llama 3.2 11B Vision with a B grade (Runs well). Expected decode speed: 154.0 tok/s.

How much VRAM does Llama 3.2 11B Vision need?

Llama 3.2 11B Vision (11B parameters) requires approximately 29.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.2 11B Vision?

The recommended quantization for Llama 3.2 11B Vision is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.2 11B Vision run at on B100 192GB?

On B100 192GB, Llama 3.2 11B Vision achieves approximately 154.0 tokens per second decode speed with a time-to-first-token of 1257ms using Q4_K_M quantization.

Can B100 192GB run Llama 3.2 11B Vision for coding?

For coding workloads, Llama 3.2 11B Vision on B100 192GB receives a B grade with 154.0 tok/s and 16K context.

What context window can Llama 3.2 11B Vision use on B100 192GB?

On B100 192GB, Llama 3.2 11B Vision can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for B100 192GB See all hardware for Llama 3.2 11B Vision

Embed this result▼

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<iframe src="https://willitrunai.com/embed/llama-3.2-11b-vision-on-b100-192gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

154.0 tok/s

686 ms

16K

Coding

Runs well

154.0 tok/s

1257 ms

16K

Agentic Coding

Runs well

154.0 tok/s

1829 ms

16K

Reasoning

Runs well

154.0 tok/s

1486 ms

16K

RAG

Runs well

154.0 tok/s

2286 ms

16K

Llama 3.2 11B Vision inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

154.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

122.7

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

110.7

Fits

RTX 3090 24GB

24 GB

Q4_K_M

105.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

97.9

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

89.2

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

74.3

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

70.5

Fits

RTX 4070 12GB

12 GB

Q4_K_M

60.6

Tight

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

48.5

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

48.5

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

38.5

Fits

RTX 3060 12GB

12 GB

Q4_K_M

38.1

Tight

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

35.2

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

29.6

Fits

RTX 4060 8GB

8 GB

Q4_K_M

13.0

Too big

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.3 GB	Low	C51
Q3_K_S	3	5.4 GB	Low	C51
NVFP4	4	6.2 GB	Medium	C51
Q4_K_M	4	6.7 GB	Medium	C51
Q5_K_M	5	7.9 GB	High	C51
Q6_K	6	9.0 GB	High	C51
Q8_0	8	11.8 GB	Very High	C51
F16Best for your GPU	16	22.5 GB	Maximum	C52

Frequently asked questions

Can B100 192GB run Llama 3.2 11B Vision?

Yes, B100 192GB can run Llama 3.2 11B Vision with a B grade (Runs well). Expected decode speed: 154.0 tok/s.

How much VRAM does Llama 3.2 11B Vision need?

Llama 3.2 11B Vision (11B parameters) requires approximately 29.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.2 11B Vision?

The recommended quantization for Llama 3.2 11B Vision is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.2 11B Vision run at on B100 192GB?

On B100 192GB, Llama 3.2 11B Vision achieves approximately 154.0 tokens per second decode speed with a time-to-first-token of 1257ms using Q4_K_M quantization.

Can B100 192GB run Llama 3.2 11B Vision for coding?

For coding workloads, Llama 3.2 11B Vision on B100 192GB receives a B grade with 154.0 tok/s and 16K context.

What context window can Llama 3.2 11B Vision use on B100 192GB?

On B100 192GB, Llama 3.2 11B Vision can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.