Can Ministral 3 3B run on GTX 1070 Ti 8GB?

YES — Runs Great

A77Great

Estimated from fit model

Ministral 3 3B needs ~5.8 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: vLLMCapacity: RoomyBandwidth: LowStack: OptimizedBottleneck: 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) — 5.8 GB, 42.0 tok/s, Runs well

5.8 GB required8.0 GB available

73% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

65K

Memory

5.8 GB / 8.0 GB

Memory breakdown

Weights1.8 GB

KV Cache0.7 GB

Runtime2.4 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsMinistral 3 3B on GTX 1070 Ti 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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	A	Runs well	42.0 tok/s	2514 ms	65K
Coding	A	Runs well	42.0 tok/s	4610 ms	65K
Agentic Coding	A	Runs well	42.0 tok/s	6705 ms	65K
Reasoning	A	Runs well	42.0 tok/s	5448 ms	65K
RAG	A	Runs well	42.0 tok/s	8381 ms	65K

Inference speed

Ministral 3 3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Ministral 3 3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~42 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	42.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	42.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	42.0

Quantization options

How Ministral 3 3B (3B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	A73
Q3_K_S	3	1.5 GB	Low	A73
NVFP4	4

Get started

Copy-paste commands to run Ministral 3 3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Ministral-3-3B-Instruct-2512" \
  --hf-file "Ministral-3-3B-Instruct-2512-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your GTX 1070 Ti 8GB can run

Model	Params	Grade	Decode	Capabilities
Phi-4 Mini Reasoning 4B	3.8B	S	53.2 tok/s
Gemma 4 E2B	5.1B	A	42.2 tok/s

Frequently asked questions

Can GTX 1070 Ti 8GB run Ministral 3 3B?

Yes, GTX 1070 Ti 8GB can run Ministral 3 3B with a A grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does Ministral 3 3B need?

Ministral 3 3B (3B parameters) requires approximately 5.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 3 3B?

The recommended quantization for Ministral 3 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 3 3B run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, Ministral 3 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run Ministral 3 3B for coding?

For coding workloads, Ministral 3 3B on GTX 1070 Ti 8GB receives a A grade with 42.0 tok/s and 65K context.

What context window can Ministral 3 3B use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, Ministral 3 3B can safely use up to 65K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for GTX 1070 Ti 8GB See all hardware for Ministral 3 3B

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