Can mistral small 3.1 24b instruct 2503 hf run on Intel Arc B580 12GB?

YES — With Q2_K

D31Poor

Estimated from fit model

mistral small 3.1 24b instruct 2503 hf needs ~14.3 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q2_K quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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.

mistral small 3.1 24b instruct 2503 hf at Q4_K_M needs 19.6 GB — too much for Intel Arc B580 12GB (12.0 GB). Runs at Q2_K (14.3 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.6 GB, exceeds 12.0 GB available

19.6 GB required12.0 GB available

163% VRAM needed

7.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.2 tok/s

TTFT

45843 ms

Safe context

Memory

19.6 GB / 12.0 GB

Offload

40%

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsmistral small 3.1 24b instruct 2503 hf on Intel Arc B580 12GB
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: 4.2 tok/s decode · 45.8s TTFT (warm) · 11 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	4.9 tok/s	21473 ms	4K
Coding	F	Too heavy	4.2 tok/s	45843 ms	4K
Agentic Coding	F	Too heavy	3.2 tok/s	87713 ms	4K
Reasoning	F	Too heavy	4.2 tok/s	54178 ms	4K
RAG	F	Too heavy	3.2 tok/s	109641 ms	4K

Inference speed

mistral small 3.1 24b instruct 2503 hf inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for mistral small 3.1 24b instruct 2503 hf at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~82 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	82.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	52.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	47.2	Tight
RTX 3090 24GB	24 GB	Q4_K_M	44.8	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	38.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	34.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	34.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	31.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	30.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	19.1	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	16.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	15.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	6.7	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	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 mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	F0
Q3_K_S	3	11.8 GB	Low	F0
NVFP4	4	13.4 GB	Medium	F0
Q4_K_M	4	14.6 GB	Medium	F0
Q5_K_M	5	17.3 GB	High	F0
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0

Get started

Copy-paste commands to run mistral small 3.1 24b instruct 2503 hf on your machine.

Run

lms load hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien mistral small 3.1 24b instruct 2503 hf

Intel Arc Pro B60 24GBOpción económica

24 GB VRAM (+12)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.16.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$599 MSRP

RTX 5090 32GBMayor salto

32 GB VRAM (+20)1792 GB/s (+1336)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.65.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,999 MSRP

Intel Data Center GPU Max 1550 128GBMejor relación calidad-precio

128 GB VRAM (+116)3200 GB/s (+2744)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.137.7 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$15,000 MSRP

Gaudi 3 128GBMejora Intel

128 GB VRAM (+116)3700 GB/s (+3244)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.176.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc B580 12GB run mistral small 3.1 24b instruct 2503 hf?

Yes, Intel Arc B580 12GB can run mistral small 3.1 24b instruct 2503 hf at Q2_K quantization (Very compromised (needs ~1.5 GB host RAM)). The recommended Q4_K_M requires 19.6 GB which exceeds available memory, but at Q2_K it needs only 14.3 GB. Expected decode speed: 10.7 tok/s.

How much VRAM does mistral small 3.1 24b instruct 2503 hf need?

mistral small 3.1 24b instruct 2503 hf (24B parameters) requires approximately 19.6 GB at Q4_K_M quantization. On Intel Arc B580 12GB, it fits at Q2_K using 14.3 GB.

What is the best quantization for mistral small 3.1 24b instruct 2503 hf?

The recommended quantization is Q4_K_M, but on Intel Arc B580 12GB the best fitting quantization is Q2_K, which uses 14.3 GB.

What speed will mistral small 3.1 24b instruct 2503 hf run at on Intel Arc B580 12GB?

On Intel Arc B580 12GB, mistral small 3.1 24b instruct 2503 hf achieves approximately 10.7 tokens per second decode speed with a time-to-first-token of 18143ms using Q2_K quantization.

Can Intel Arc B580 12GB run mistral small 3.1 24b instruct 2503 hf for coding?

For coding workloads, mistral small 3.1 24b instruct 2503 hf on Intel Arc B580 12GB receives a F grade with 4.2 tok/s and 4K context.

What context window can mistral small 3.1 24b instruct 2503 hf use on Intel Arc B580 12GB?

On Intel Arc B580 12GB, mistral small 3.1 24b instruct 2503 hf can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if mistral small 3.1 24b instruct 2503 hf feels slow on Intel Arc B580 12GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Would CUDA be a better path than Intel Arc B580 12GB for mistral small 3.1 24b instruct 2503 hf?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc B580 12GB See all hardware for mistral small 3.1 24b instruct 2503 hf

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf-on-arc-b580-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: