Can DeepSeek R1 Distill Llama 8B run on GTX 1070 Ti 8GB?

Q: What should I upgrade first if DeepSeek R1 Distill Llama 8B feels slow on GTX 1070 Ti 8GB?

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

YES — With Offload

C51Usable

Estimated from fit model

DeepSeek R1 Distill Llama 8B needs ~7.8 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~31 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, 31.0 tok/s, Runs with offload

7.8 GB required8.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

31.0 tok/s

TTFT

6255 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 feelsDeepSeek R1 Distill Llama 8B on GTX 1070 Ti 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: 31.0 tok/s decode · 6.3s TTFT (warm) · 77 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.

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

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	31.0 tok/s	3412 ms	19K
Coding	C	Runs with offload	31.0 tok/s	6255 ms	19K
Agentic Coding	D	Very compromised	18.5 tok/s	15205 ms	19K
Reasoning	C	Runs with offload	31.0 tok/s	7392 ms	19K
RAG	D	Very compromised	18.5 tok/s	19007 ms	19K

Quantization options

How DeepSeek R1 Distill Llama 8B (8B params) fits at each quantization level on GTX 1070 Ti 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

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 3060 12GBBudget pick

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

Raises estimated decode speed by about 57%.48.7 tok/s decode

Raises estimated decode speed by about 57%.

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

~$329 MSRP

RTX 5060 Ti 16GBBest value

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

Raises estimated decode speed by about 84%.56.9 tok/s decode

Raises estimated decode speed by about 84%.

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

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)288 GB/s (+32)

Raises estimated decode speed by about 39%.43.1 tok/s decode

Raises estimated decode speed by about 39%.

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

~$499 MSRP

Frequently asked questions

Can GTX 1070 Ti 8GB run DeepSeek R1 Distill Llama 8B?

Yes, GTX 1070 Ti 8GB can run DeepSeek R1 Distill Llama 8B with a C grade (Runs with offload). Expected decode speed: 31.0 tok/s.

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

DeepSeek R1 Distill Llama 8B (8B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.

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

The recommended quantization for DeepSeek R1 Distill Llama 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill Llama 8B run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, DeepSeek R1 Distill Llama 8B achieves approximately 31.0 tokens per second decode speed with a time-to-first-token of 6255ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run DeepSeek R1 Distill Llama 8B for coding?

For coding workloads, DeepSeek R1 Distill Llama 8B on GTX 1070 Ti 8GB receives a C grade with 31.0 tok/s and 19K context.

What context window can DeepSeek R1 Distill Llama 8B use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, DeepSeek R1 Distill Llama 8B can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1070 Ti 8GB See all hardware for DeepSeek R1 Distill Llama 8B

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