Can Qwen2.5 1.5B Instruct run on B100 192GB?

YES — Runs Great

D40Poor

Estimated from fit model

Qwen2.5 1.5B Instruct needs ~21.5 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~21 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) — 21.5 GB, 21.0 tok/s, Runs well

21.5 GB required192.0 GB available

11% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

15.5M

Memory

21.5 GB / 192.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsQwen2.5 1.5B Instruct 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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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	D	Runs well	21.0 tok/s	5029 ms	13.7M
Coding	D	Runs well	21.0 tok/s	9219 ms	15.5M
Agentic Coding	D	Runs well	21.0 tok/s	13410 ms	15.5M
Reasoning	D	Runs well	21.0 tok/s	10895 ms	15.5M
RAG	D	Runs well	21.0 tok/s	16762 ms	15.5M

Inference speed

Qwen2.5 1.5B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen2.5 1.5B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~29 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	28.5	Fits
RTX 4090 24GB	24 GB	Q4_K_M	24.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	24.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	21.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	21.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	21.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	21.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	21.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	21.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	21.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	21.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	21.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	21.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	21.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	21.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.0	Fits

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 Qwen2.5 1.5B Instruct (1.5B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	D37
Q3_K_S	3	0.7 GB	Low	D37
NVFP4	4	0.8 GB	Medium	D37
Q4_K_M	4	0.9 GB	Medium	D37
Q5_K_M	5	1.1 GB	High	D37
Q6_K	6	1.2 GB	High	D37
Q8_0	8	1.6 GB	Very High	D37
F16Best for your GPU	16	3.1 GB	Maximum	D37

Get started

Copy-paste commands to run Qwen2.5 1.5B Instruct on your machine.

Run

lms load hf-qwen--qwen2-5-1-5b-instruct-gguf && lms server start

Frequently asked questions

Can B100 192GB run Qwen2.5 1.5B Instruct?

Yes, B100 192GB can run Qwen2.5 1.5B Instruct with a D grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Qwen2.5 1.5B Instruct need?

Qwen2.5 1.5B Instruct (1.5B parameters) requires approximately 21.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen2.5 1.5B Instruct?

The recommended quantization for Qwen2.5 1.5B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen2.5 1.5B Instruct run at on B100 192GB?

On B100 192GB, Qwen2.5 1.5B Instruct achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can B100 192GB run Qwen2.5 1.5B Instruct for coding?

For coding workloads, Qwen2.5 1.5B Instruct on B100 192GB receives a D grade with 21.0 tok/s and 15.5M context.

What context window can Qwen2.5 1.5B Instruct use on B100 192GB?

On B100 192GB, Qwen2.5 1.5B Instruct can safely use up to 15.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for B100 192GB See all hardware for Qwen2.5 1.5B Instruct

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-qwen--qwen2-5-1-5b-instruct-gguf-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

21.0 tok/s

5029 ms

13.7M

Coding

Runs well

21.0 tok/s

9219 ms

15.5M

Agentic Coding

Runs well

21.0 tok/s

13410 ms

15.5M

Reasoning

Runs well

21.0 tok/s

10895 ms

15.5M

RAG

Runs well

21.0 tok/s

16762 ms

15.5M

Qwen2.5 1.5B Instruct inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

28.5

Fits

RTX 4090 24GB

24 GB

Q4_K_M

24.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

24.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

21.0

Fits

RTX 4070 12GB

12 GB

Q4_K_M

21.0

Fits

RTX 3060 12GB

12 GB

Q4_K_M

21.0

Fits

RTX 4060 8GB

8 GB

Q4_K_M

21.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

21.0

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

21.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

21.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

21.0

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

21.0

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

21.0

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

21.0

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

21.0

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

21.0

Fits

Quantization options

How Qwen2.5 1.5B Instruct (1.5B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	D37
Q3_K_S	3	0.7 GB	Low	D37
NVFP4	4	0.8 GB	Medium	D37
Q4_K_M	4	0.9 GB	Medium	D37
Q5_K_M	5	1.1 GB	High	D37
Q6_K	6	1.2 GB	High	D37
Q8_0	8	1.6 GB	Very High	D37
F16Best for your GPU	16	3.1 GB	Maximum	D37

Frequently asked questions

Can B100 192GB run Qwen2.5 1.5B Instruct?

Yes, B100 192GB can run Qwen2.5 1.5B Instruct with a D grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Qwen2.5 1.5B Instruct need?

Qwen2.5 1.5B Instruct (1.5B parameters) requires approximately 21.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen2.5 1.5B Instruct?

The recommended quantization for Qwen2.5 1.5B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen2.5 1.5B Instruct run at on B100 192GB?

On B100 192GB, Qwen2.5 1.5B Instruct achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can B100 192GB run Qwen2.5 1.5B Instruct for coding?

For coding workloads, Qwen2.5 1.5B Instruct on B100 192GB receives a D grade with 21.0 tok/s and 15.5M context.

What context window can Qwen2.5 1.5B Instruct use on B100 192GB?

On B100 192GB, Qwen2.5 1.5B Instruct can safely use up to 15.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.