Can DeepSeek R1 Distill Llama 8B run on GTX 1060 6GB?

Q: What should I upgrade first if DeepSeek R1 Distill Llama 8B feels slow on GTX 1060 6GB?

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.

YES — With Q3_K_S

D38Poor

Estimated from fit model

DeepSeek R1 Distill Llama 8B needs ~6.7 GB VRAM. GTX 1060 6GB has 6.0 GB. With Q3_K_S quantization, expect ~16 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: 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.

DeepSeek R1 Distill Llama 8B at Q4_K_M needs 7.6 GB — too much for GTX 1060 6GB (6.0 GB). Runs at Q3_K_S (6.7 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.6 GB, exceeds 6.0 GB available

7.6 GB required6.0 GB available

127% VRAM needed

1.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.0 tok/s

TTFT

19313 ms

Safe context

Memory

7.6 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek R1 Distill Llama 8B on GTX 1060 6GB
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: 10.0 tok/s decode · 19.3s TTFT (warm) · 25 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	D	Very compromised (needs ~0.8 GB host RAM)	11.5 tok/s	9163 ms	4K
Coding	F	Too heavy	10.0 tok/s	19313 ms	4K
Agentic Coding	F	Too heavy	7.8 tok/s	36246 ms	4K
Reasoning	F	Too heavy	10.0 tok/s	22824 ms	4K
RAG	F	Too heavy	7.8 tok/s	45308 ms	4K

Quantization options

How DeepSeek R1 Distill Llama 8B (8B params) fits at each quantization level on GTX 1060 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	F0

Get started

Copy-paste commands to run DeepSeek R1 Distill Llama 8B on your machine.

Run

lms load hf-unsloth--deepseek-r1-distill-llama-8b-gguf && lms server start

Upgrade options

Hardware that runs DeepSeek R1 Distill Llama 8B well

RTX 3050 8GBBudget pick

8 GB VRAM (+2)224 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.30.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$249 MSRP

RTX 5060 8GBBest value

8 GB VRAM (+2)448 GB/s (+256)

Makes the model fit on the accelerator instead of staying completely out of reach.56 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$299 MSRP

RTX 4060 8GBNVIDIA upgrade

8 GB VRAM (+2)272 GB/s (+80)

Makes the model fit on the accelerator instead of staying completely out of reach.40.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$299 MSRP

Frequently asked questions

Can GTX 1060 6GB run DeepSeek R1 Distill Llama 8B?

Yes, GTX 1060 6GB can run DeepSeek R1 Distill Llama 8B at Q3_K_S quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 7.6 GB which exceeds available memory, but at Q3_K_S it needs only 6.7 GB. Expected decode speed: 15.6 tok/s.

How much VRAM does DeepSeek R1 Distill Llama 8B need?

DeepSeek R1 Distill Llama 8B (8B parameters) requires approximately 7.6 GB at Q4_K_M quantization. On GTX 1060 6GB, it fits at Q3_K_S using 6.7 GB.

What is the best quantization for DeepSeek R1 Distill Llama 8B?

The recommended quantization is Q4_K_M, but on GTX 1060 6GB the best fitting quantization is Q3_K_S, which uses 6.7 GB.

What speed will DeepSeek R1 Distill Llama 8B run at on GTX 1060 6GB?

On GTX 1060 6GB, DeepSeek R1 Distill Llama 8B achieves approximately 15.6 tokens per second decode speed with a time-to-first-token of 12409ms using Q3_K_S quantization.

Can GTX 1060 6GB run DeepSeek R1 Distill Llama 8B for coding?

For coding workloads, DeepSeek R1 Distill Llama 8B on GTX 1060 6GB receives a F grade with 10.0 tok/s and 4K context.

What context window can DeepSeek R1 Distill Llama 8B use on GTX 1060 6GB?

On GTX 1060 6GB, DeepSeek R1 Distill Llama 8B can safely use up to 5K tokens of context at Q3_K_S quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1060 6GB See all hardware for DeepSeek R1 Distill Llama 8B

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