Can InternLM 7B run on GTX 1070 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

InternLM 7B needs ~14.1 GB but GTX 1070 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 14.1 GB, exceeds 8.0 GB available

14.1 GB required8.0 GB available

176% VRAM needed

6.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.5 tok/s

TTFT

25977 ms

Safe context

Memory

14.1 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsInternLM 7B 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: 7.5 tok/s decode · 26.0s TTFT (warm) · 19 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 14.1 GB, but this setup only exposes 8.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	15.2 tok/s	6942 ms	4K
Coding	F	Too heavy	7.5 tok/s	25977 ms	4K
Agentic Coding	F	Too heavy	5.3 tok/s	53073 ms	4K
Reasoning	F	Too heavy	7.5 tok/s	30700 ms	4K
RAG	F	Too heavy	5.3 tok/s	66342 ms	4K

Quantization options

How InternLM 7B (7B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A74
Q3_K_S	3	3.4 GB	Low	A74
NVFP4	4

Upgrade options

Hardware that runs InternLM 7B well

RTX 5060 Ti 16GBBudget pick

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

Makes the model fit on the accelerator instead of staying completely out of reach.65 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.

~$449 MSRP

RTX 4060 Ti 16GBBest value

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

Makes the model fit on the accelerator instead of staying completely out of reach.49.2 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.

~$499 MSRP

RTX 2000 Ada 16GBNVIDIA upgrade

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

Makes the model fit on the accelerator instead of staying completely out of reach.51.3 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.

~$625 MSRP

Frequently asked questions

Can GTX 1070 8GB run InternLM 7B?

No, InternLM 7B requires more memory than GTX 1070 8GB provides.

How much VRAM does InternLM 7B need?

InternLM 7B (7B parameters) requires approximately 14.1 GB of memory with Q4_K_M quantization.

What is the best quantization for InternLM 7B?

The recommended quantization for InternLM 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will InternLM 7B run at on GTX 1070 8GB?

On GTX 1070 8GB, InternLM 7B achieves approximately 7.5 tokens per second decode speed with a time-to-first-token of 25977ms using Q4_K_M quantization.

Can GTX 1070 8GB run InternLM 7B for coding?

For coding workloads, InternLM 7B on GTX 1070 8GB receives a F grade with 7.5 tok/s and 4K context.

What context window can InternLM 7B use on GTX 1070 8GB?

On GTX 1070 8GB, InternLM 7B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if InternLM 7B feels slow on GTX 1070 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1070 8GB See all hardware for InternLM 7B

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