Can Qwen3.5 9B run on Tesla P40 24GB?

YES — Runs Great

C48Usable

Estimated from fit model

Qwen3.5 9B needs ~10.1 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 10.1 GB, 37.2 tok/s, Runs well

10.1 GB required24.0 GB available

42% VRAM used

Fit status

Runs well

Decode

37.2 tok/s

TTFT

5207 ms

Safe context

226K

Memory

10.1 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen3.5 9B on Tesla P40 24GB
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: 37.2 tok/s decode · 5.2s TTFT (warm) · 93 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	C	Runs well	37.2 tok/s	2840 ms	226K
Coding	C	Runs well	37.2 tok/s	5207 ms	226K
Agentic Coding	C	Runs well	37.2 tok/s	7573 ms	226K
Reasoning	C	Runs well	37.2 tok/s	6153 ms	226K
RAG	C	Runs well	37.2 tok/s	9466 ms	226K

Quantization options

How Qwen3.5 9B (9B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C45
Q3_K_S	3	4.4 GB	Low	C46
NVFP4	4	5.0 GB	Medium	C46
Q4_K_M	4	5.5 GB	Medium	C46
Q5_K_M	5	6.5 GB	High	C47
Q6_K	6	7.4 GB	High	C47
Q8_0	8	9.6 GB	Very High	C49
F16Best for your GPU	16	18.5 GB	Maximum	C50

Get started

Copy-paste commands to run Qwen3.5 9B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "unsloth/Qwen3.5-9B-GGUF" \
  --hf-file "Qwen3.5-9B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade-Optionen

Hardware, die Qwen3.5 9B gut ausführt

RTX 5090 32GBGünstige Wahl

32 GB VRAM (+8)1792 GB/s (+1446)

Raises estimated decode speed by about 239%.126 tok/s Dekodierung

Raises estimated decode speed by about 239%.

Adds memory headroom for longer context windows and future model growth.

ca. $1,999 MSRP

RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-Verhältnis

32 GB VRAM (+8)896 GB/s (+550)

Raises estimated decode speed by about 239%.126 tok/s Dekodierung

Raises estimated decode speed by about 239%.

Adds memory headroom for longer context windows and future model growth.

ca. $2,499 MSRP

RTX 5000 Ada 32GBNVIDIA-Upgrade

32 GB VRAM (+8)576 GB/s (+230)

Raises estimated decode speed by about 126%.83.9 tok/s Dekodierung

Raises estimated decode speed by about 126%.

Adds memory headroom for longer context windows and future model growth.

ca. $4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run Qwen3.5 9B?

Yes, Tesla P40 24GB can run Qwen3.5 9B with a C grade (Runs well). Expected decode speed: 37.2 tok/s.

How much VRAM does Qwen3.5 9B need?

Qwen3.5 9B (9B parameters) requires approximately 10.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 9B?

The recommended quantization for Qwen3.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 9B run at on Tesla P40 24GB?

On Tesla P40 24GB, Qwen3.5 9B achieves approximately 37.2 tokens per second decode speed with a time-to-first-token of 5207ms using Q4_K_M quantization.

Can Tesla P40 24GB run Qwen3.5 9B for coding?

For coding workloads, Qwen3.5 9B on Tesla P40 24GB receives a C grade with 37.2 tok/s and 226K context.

What context window can Qwen3.5 9B use on Tesla P40 24GB?

On Tesla P40 24GB, Qwen3.5 9B can safely use up to 226K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for Qwen3.5 9B

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