Can MiniCPM-V 2.6 8B run on RTX 4070 Super 12GB?

YES — Runs Great

S87Excellent

Estimated from fit model

MiniCPM-V 2.6 8B needs ~9.2 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~80 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 9.2 GB, 85.5 tok/s, Runs well

9.2 GB required12.0 GB available

77% VRAM used

Fit status

Runs well

Decode

85.5 tok/s

TTFT

2265 ms

Safe context

Memory

9.2 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsMiniCPM-V 2.6 8B on RTX 4070 Super 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: 85.5 tok/s decode · 2.3s TTFT (warm) · 214 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	85.5 tok/s	1235 ms	2K
Coding	S	Runs well	79.5 tok/s	2434 ms	2K
Agentic Coding	A	Tight fit	85.5 tok/s	3294 ms	2K
Reasoning	S	Runs well	85.5 tok/s	2676 ms	2K
RAG	A	Tight fit	85.5 tok/s	4117 ms	2K

Quantization options

How MiniCPM-V 2.6 8B (8B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A80
Q3_K_S	3	3.9 GB	Low	A81
NVFP4	4	4.5 GB	Medium	A82
Q4_K_M	4	4.9 GB	Medium	A82
Q5_K_M	5	5.8 GB	High	A83
Q6_K	6	6.6 GB	High	A83
Q8_0Best for your GPU	8	8.6 GB	Very High	A82
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run MiniCPM-V 2.6 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "openbmb/MiniCPM-V-2_6" \
  --hf-file "MiniCPM-V-2_6-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your RTX 4070 Super 12GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	76 tok/s
Qwen 3 14B	14B	A	29.3 tok/s
Ministral 3 14B	14B	A	29.1 tok/s
Phi-4 14B	14B	A	26.5 tok/s
Qwen 2.5 14B	14B	A	27.1 tok/s

Frequently asked questions

Can RTX 4070 Super 12GB run MiniCPM-V 2.6 8B?

Yes, RTX 4070 Super 12GB can run MiniCPM-V 2.6 8B with a S grade (Runs well). Expected decode speed: 79.5 tok/s.

How much VRAM does MiniCPM-V 2.6 8B need?

MiniCPM-V 2.6 8B (8B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.

What is the best quantization for MiniCPM-V 2.6 8B?

The recommended quantization for MiniCPM-V 2.6 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will MiniCPM-V 2.6 8B run at on RTX 4070 Super 12GB?

On RTX 4070 Super 12GB, MiniCPM-V 2.6 8B achieves approximately 79.5 tokens per second decode speed with a time-to-first-token of 2434ms using Q4_K_M quantization.

Can RTX 4070 Super 12GB run MiniCPM-V 2.6 8B for coding?

For coding workloads, MiniCPM-V 2.6 8B on RTX 4070 Super 12GB receives a S grade with 79.5 tok/s and 2K context.

What context window can MiniCPM-V 2.6 8B use on RTX 4070 Super 12GB?

On RTX 4070 Super 12GB, MiniCPM-V 2.6 8B can safely use up to 2K tokens of context. The model's official context limit is 2K, but available memory constrains the safe maximum.

See all results for RTX 4070 Super 12GB See all hardware for MiniCPM-V 2.6 8B

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