Can Llama 3 8B Instruct 32k v0.1 run on RTX 4070 Super 12GB?

YES — Runs Great

B56Good

Estimated from fit model

Llama 3 8B Instruct 32k v0.1 needs ~8.2 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) — 8.2 GB, 79.5 tok/s, Runs well

8.2 GB required12.0 GB available

68% VRAM used

Fit status

Runs well

Decode

79.5 tok/s

TTFT

2434 ms

Safe context

81K

Memory

8.2 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsLlama 3 8B Instruct 32k v0.1 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: 79.5 tok/s decode · 2.4s TTFT (warm) · 199 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	B	Runs well	79.5 tok/s	1328 ms	81K
Coding	B	Runs well	79.5 tok/s	2434 ms	81K
Agentic Coding	B	Runs well	79.5 tok/s	3541 ms	81K
Reasoning	B	Runs well	79.5 tok/s	2877 ms	81K
RAG	B	Runs well	79.5 tok/s	4426 ms	81K

Quantization options

How Llama 3 8B Instruct 32k v0.1 (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	C50
Q3_K_S	3	3.9 GB	Low	C51
NVFP4	4

Get started

Copy-paste commands to run Llama 3 8B Instruct 32k v0.1 on your machine.

Run

lms load hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf && lms server start

Frequently asked questions

Can RTX 4070 Super 12GB run Llama 3 8B Instruct 32k v0.1?

Yes, RTX 4070 Super 12GB can run Llama 3 8B Instruct 32k v0.1 with a B grade (Runs well). Expected decode speed: 79.5 tok/s.

How much VRAM does Llama 3 8B Instruct 32k v0.1 need?

Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3 8B Instruct 32k v0.1?

The recommended quantization for Llama 3 8B Instruct 32k v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3 8B Instruct 32k v0.1 run at on RTX 4070 Super 12GB?

On RTX 4070 Super 12GB, Llama 3 8B Instruct 32k v0.1 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 Llama 3 8B Instruct 32k v0.1 for coding?

For coding workloads, Llama 3 8B Instruct 32k v0.1 on RTX 4070 Super 12GB receives a B grade with 79.5 tok/s and 81K context.

What context window can Llama 3 8B Instruct 32k v0.1 use on RTX 4070 Super 12GB?

On RTX 4070 Super 12GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 81K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4070 Super 12GB See all hardware for Llama 3 8B Instruct 32k v0.1

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