Can Qwen 3.5 2B run on Tesla P100 16GB?

YES — Runs Great

B68Good

Estimated from fit model

Qwen 3.5 2B needs ~5.7 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 5.7 GB, 28.0 tok/s, Runs well

5.7 GB required16.0 GB available

36% VRAM used

Fit status

Runs well

Decode

28.0 tok/s

TTFT

6914 ms

Safe context

112K

Memory

5.7 GB / 16.0 GB

Memory breakdown

Weights1.2 GB

KV Cache1.7 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 3.5 2B on Tesla P100 16GB
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: 28.0 tok/s decode · 6.9s TTFT (warm) · 70 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	28.0 tok/s	3771 ms	112K
Coding	B	Runs well	28.0 tok/s	6914 ms	112K
Agentic Coding	A	Runs well	28.0 tok/s	10057 ms	112K
Reasoning	B	Runs well	28.0 tok/s	8171 ms	112K
RAG	A	Runs well	28.0 tok/s	12571 ms	112K

Quantization options

How Qwen 3.5 2B (2B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.8 GB	Low	B68
Q3_K_S	3	1.0 GB	Low	B68
NVFP4	4

Get started

Copy-paste commands to run Qwen 3.5 2B on your machine.

Run

ollama run qwen3.5:2b

Upgrade options

Hardware that runs Qwen 3.5 2B well

MacBook Pro M3 24GBBudget pick

24 GB Unified (+8)

This setup is broadly balanced for this model.28 tok/s decode

~$1,099 MSRP

MacBook Air M3 24GBBest value

24 GB Unified (+8)

This setup is broadly balanced for this model.28 tok/s decode

~$1,099 MSRP

Frequently asked questions

Can Tesla P100 16GB run Qwen 3.5 2B?

Yes, Tesla P100 16GB can run Qwen 3.5 2B with a B grade (Runs well). Expected decode speed: 28.0 tok/s.

How much VRAM does Qwen 3.5 2B need?

Qwen 3.5 2B (2B parameters) requires approximately 5.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 2B?

The recommended quantization for Qwen 3.5 2B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 2B run at on Tesla P100 16GB?

On Tesla P100 16GB, Qwen 3.5 2B achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6914ms using Q4_K_M quantization.

Can Tesla P100 16GB run Qwen 3.5 2B for coding?

For coding workloads, Qwen 3.5 2B on Tesla P100 16GB receives a B grade with 28.0 tok/s and 112K context.

What context window can Qwen 3.5 2B use on Tesla P100 16GB?

On Tesla P100 16GB, Qwen 3.5 2B can safely use up to 112K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Tesla P100 16GB See all hardware for Qwen 3.5 2B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-3.5-2b-on-tesla-p100-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: