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

YES — With Offload

C51Usable

Estimated from fit model

Llama 3 8B Instruct 32k v0.1 needs ~7.8 GB VRAM. RTX 4070 Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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) — 7.8 GB, 39.9 tok/s, Runs with offload

7.8 GB required8.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

39.9 tok/s

TTFT

4856 ms

Safe context

19K

Memory

7.8 GB / 8.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsLlama 3 8B Instruct 32k v0.1 on RTX 4070 Laptop 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: 39.9 tok/s decode · 4.9s TTFT (warm) · 100 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	39.9 tok/s	2649 ms	19K
Coding	C	Runs with offload	39.9 tok/s	4856 ms	19K
Agentic Coding	D	Very compromised (needs ~0.4 GB host RAM)	24.7 tok/s	11386 ms	19K
Reasoning	C	Runs with offload	39.9 tok/s	5739 ms	19K
RAG	D	Very compromised (needs ~0.4 GB host RAM)	24.7 tok/s	14233 ms	19K

Quantization options

How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on RTX 4070 Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	C54
NVFP4	4	4.5 GB	Medium	C53
Q4_K_MBest for your GPU	4	4.9 GB	Medium	C53
Q5_K_M	5	5.8 GB	High	F0
Q6_K	6	6.6 GB	High	F0
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

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

升级选项

能流畅运行 Llama 3 8B Instruct 32k v0.1 的硬件

RTX 3060 12GB经济之选

12 GB VRAM (+4)360 GB/s (+104)

Adds memory headroom for longer context windows and future model growth.48.7 tok/s 解码

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RTX 5060 Ti 16GB最佳性价比

16 GB VRAM (+8)448 GB/s (+192)

Raises estimated decode speed by about 43%.56.9 tok/s 解码

Raises estimated decode speed by about 43%.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RTX 5070 12GBNVIDIA 升级

12 GB VRAM (+4)672 GB/s (+416)

Raises estimated decode speed by about 118%.86.8 tok/s 解码

Raises estimated decode speed by about 118%.

Adds memory headroom for longer context windows and future model growth.

~$549 MSRP

Frequently asked questions

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

Yes, RTX 4070 Laptop 8GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs with offload). Expected decode speed: 39.9 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 7.8 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 Laptop 8GB?

On RTX 4070 Laptop 8GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 39.9 tokens per second decode speed with a time-to-first-token of 4856ms using Q4_K_M quantization.

Can RTX 4070 Laptop 8GB run Llama 3 8B Instruct 32k v0.1 for coding?

For coding workloads, Llama 3 8B Instruct 32k v0.1 on RTX 4070 Laptop 8GB receives a C grade with 39.9 tok/s and 19K context.

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

On RTX 4070 Laptop 8GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3 8B Instruct 32k v0.1 feels slow on RTX 4070 Laptop 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

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

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