willitrun·ai

Can Mistral Small 24B Instruct 2501 run on GTX 1080 8GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

Mistral Small 24B Instruct 2501 needs ~19.5 GB but GTX 1080 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity
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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) — 19.5 GB, exceeds 8.0 GB available
19.5 GB required8.0 GB available
244% VRAM needed

11.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

4K

Memory

19.5 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsMistral Small 24B Instruct 2501 on GTX 1080 8GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 19.5 GB, but this setup only exposes 8.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy2.0 tok/s52800 ms4K
CodingFToo heavy2.0 tok/s96800 ms4K
Agentic CodingFToo heavy2.0 tok/s140800 ms4K
ReasoningFToo heavy2.0 tok/s114400 ms4K
RAGFToo heavy2.0 tok/s176000 ms4K

Inference speed

Mistral Small 24B Instruct 2501 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Mistral Small 24B Instruct 2501 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M82.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M52.3Tight
RX 7900 XTX 24GB
24 GBQ4_K_M47.2Tight
NVIDIARTX 3090 24GB
24 GBQ4_K_M44.8Tight
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M38.0Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M34.2Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M34.2Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M31.7Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M30.1Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M21.5Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M19.1Too big
MacBook Pro M3 Max 64GB
64 GBQ4_K_M16.4Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M15.0Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M6.7Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M4.2Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M2.0Too 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 24B Instruct 2501 (24B params) fits at each quantization level on GTX 1080 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowF0
Q3_K_S
3
11.8 GB
LowF0
NVFP4
4
13.4 GB
MediumF0
Q4_K_M
4
14.6 GB
MediumF0
Q5_K_M
5
17.3 GB
HighF0
Q6_K
6
19.7 GB
HighF0
Q8_0
8
25.7 GB
Very HighF0
F16
16
49.2 GB
MaximumF0

Upgrade-Optionen

Hardware, die Mistral Small 24B Instruct 2501 gut ausführt

NVIDIARTX 4000 Ada 20GBGünstige Wahl
20 GB VRAM (+12)360 GB/s (+40)
C
Makes the model fit on the accelerator instead of staying completely out of reach.13.4 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $1,250 MSRP

NVIDIARTX 3090 24GBBestes Preis-Leistungs-Verhältnis
24 GB VRAM (+16)936 GB/s (+616)
C
Makes the model fit on the accelerator instead of staying completely out of reach.44.8 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $1,499 MSRP

NVIDIARTX 4090 24GBNVIDIA-Upgrade
24 GB VRAM (+16)1008 GB/s (+688)
C
Makes the model fit on the accelerator instead of staying completely out of reach.52.3 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $1,599 MSRP

Frequently asked questions

Can GTX 1080 8GB run Mistral Small 24B Instruct 2501?

No, Mistral Small 24B Instruct 2501 requires more memory than GTX 1080 8GB provides.

How much VRAM does Mistral Small 24B Instruct 2501 need?

Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 19.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 24B Instruct 2501?

The recommended quantization for Mistral Small 24B Instruct 2501 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 24B Instruct 2501 run at on GTX 1080 8GB?

On GTX 1080 8GB, Mistral Small 24B Instruct 2501 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can GTX 1080 8GB run Mistral Small 24B Instruct 2501 for coding?

For coding workloads, Mistral Small 24B Instruct 2501 on GTX 1080 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Mistral Small 24B Instruct 2501 use on GTX 1080 8GB?

On GTX 1080 8GB, Mistral Small 24B Instruct 2501 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Small 24B Instruct 2501 feels slow on GTX 1080 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1080 8GBSee all hardware for Mistral Small 24B Instruct 2501
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