Can Qwen 3 8B run on GTX 1070 8GB?

BARELY — Tight on Memory

A79Great

Estimated from fit model

Qwen 3 8B needs ~8.8 GB VRAM. GTX 1070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 8.8 GB, 19.8 tok/s, Very compromised (needs ~0.4 GB host RAM)

8.8 GB required8.0 GB available

110% VRAM needed

0.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

19.8 tok/s

TTFT

9779 ms

Safe context

10K

Memory

8.8 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen 3 8B on GTX 1070 8GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 19.8 tok/s decode · 9.8s TTFT (warm) · 50 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload	33.3 tok/s	3174 ms	10K
Coding	A	Very compromised (needs ~0.4 GB host RAM)	19.8 tok/s	9779 ms	10K
Agentic Coding	F	Too heavy	12.1 tok/s	23232 ms	10K
Reasoning	A	Very compromised (needs ~0.4 GB host RAM)	19.8 tok/s	11557 ms	10K
RAG	F	Too heavy	12.1 tok/s	29040 ms

Quantization options

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

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	S94
Q3_K_S	3	3.9 GB	Low	S93
NVFP4	4

Get started

Copy-paste commands to run Qwen 3 8B on your machine.

Run

ollama run qwen3:8b

Your hardware

More models your GTX 1070 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	A	15.2 tok/s

Frequently asked questions

Can GTX 1070 8GB run Qwen 3 8B?

Yes, GTX 1070 8GB can run Qwen 3 8B with a A grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 19.8 tok/s.

How much VRAM does Qwen 3 8B need?

Qwen 3 8B (8B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3 8B?

The recommended quantization for Qwen 3 8B is Q4_K_M, which balances quality and memory efficiency.

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

On GTX 1070 8GB, Qwen 3 8B achieves approximately 19.8 tokens per second decode speed with a time-to-first-token of 9779ms using Q4_K_M quantization.

Can GTX 1070 8GB run Qwen 3 8B for coding?

For coding workloads, Qwen 3 8B on GTX 1070 8GB receives a A grade with 19.8 tok/s and 10K context.

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

On GTX 1070 8GB, Qwen 3 8B can safely use up to 10K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3 8B feels slow on GTX 1070 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for GTX 1070 8GB See all hardware for Qwen 3 8B

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