VOOZH about

URL: https://willitrunai.com/can-run/hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf-on-rtx-4070-12gb

⇱ Mistral Small 3.2 24B Instruct 2506 on RTX 4070 12GB? No — …

Can Mistral Small 3.2 24B Instruct 2506 run on RTX 4070 12GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

Mistral Small 3.2 24B Instruct 2506 needs ~19.9 GB but RTX 4070 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity
Share:

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) — 19.9 GB, exceeds 12.0 GB available
19.9 GB required12.0 GB available
166% VRAM needed

7.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.7 tok/s

TTFT

28848 ms

Safe context

4K

Memory

19.9 GB / 12.0 GB

Offload

40%

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsMistral Small 3.2 24B Instruct 2506 on RTX 4070 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: 6.7 tok/s decode · 28.8s TTFT (warm) · 17 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 19.9 GB, but this setup only exposes 12.0 GB of usable VRAM.

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 heavy7.8 tok/s13481 ms4K
CodingFToo heavy6.7 tok/s28848 ms4K
Agentic CodingFToo heavy5.1 tok/s55458 ms4K
ReasoningFToo heavy6.7 tok/s34093 ms4K
RAGFToo heavy5.1 tok/s69323 ms4K

Quantization options

How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on RTX 4070 12GB (12.0 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 options

Hardware that runs Mistral Small 3.2 24B Instruct 2506 well

👁 NVIDIA
RTX 4000 Ada 20GBBudget pick
20 GB VRAM (+8)
C
Makes the model fit on the accelerator instead of staying completely out of reach.13.4 tok/s decode

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.

~$1,250 MSRP

👁 NVIDIA
RTX 3090 24GBBest value
24 GB VRAM (+12)936 GB/s (+432)
C
Makes the model fit on the accelerator instead of staying completely out of reach.44.8 tok/s decode

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.

~$1,499 MSRP

👁 NVIDIA
RTX 4090 24GBNVIDIA upgrade
24 GB VRAM (+12)1008 GB/s (+504)
C
Makes the model fit on the accelerator instead of staying completely out of reach.52.3 tok/s decode

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.

~$1,599 MSRP

Frequently asked questions

See all results for RTX 4070 12GBSee all hardware for Mistral Small 3.2 24B Instruct 2506