Can internlm2 math plus 7b IMat run on GTX 1070 Ti 8GB?

YES — Tight Fit

C50Usable

Estimated from fit model

internlm2 math plus 7b IMat needs ~7.1 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 7.1 GB, 35.4 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

35.4 tok/s

TTFT

5473 ms

Safe context

34K

Memory

7.1 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsinternlm2 math plus 7b IMat on GTX 1070 Ti 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: 35.4 tok/s decode · 5.5s TTFT (warm) · 88 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	35.4 tok/s	2985 ms	34K
Coding	C	Tight fit	35.4 tok/s	5473 ms	34K
Agentic Coding	C	Runs with offload	35.4 tok/s	7961 ms	34K
Reasoning	C	Tight fit	35.4 tok/s	6468 ms	34K
RAG	C	Runs with offload	35.4 tok/s	9951 ms	34K

Inference speed

internlm2 math plus 7b IMat inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for internlm2 math plus 7b IMat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	88.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	55.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	51.5	Fits
RTX 4060 8GB	8 GB	Q4_K_M	46.5	Tight
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	45.3	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How internlm2 math plus 7b IMat (7B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	C53
Q4_K_M	4	4.3 GB	Medium	C53
Q5_K_MBest for your GPU	5	5.0 GB	High	C52
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run internlm2 math plus 7b IMat on your machine.

Run

lms load hf-legraphista--internlm2-math-plus-7b-imat-gguf && lms server start

Opções de upgrade

Hardware que roda bem internlm2 math plus 7b IMat

RTX 3060 12GBOpção econômica

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

Raises estimated decode speed by about 57%.55.6 tok/s decodificação

Raises estimated decode speed by about 57%.

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

~$329 MSRP

RTX 5060 Ti 16GBMelhor custo-benefício

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

Raises estimated decode speed by about 84%.65 tok/s decodificação

Raises estimated decode speed by about 84%.

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

~$449 MSRP

RTX 5070 12GBUpgrade NVIDIA

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

Raises estimated decode speed by about 177%.98 tok/s decodificação

Raises estimated decode speed by about 177%.

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

~$549 MSRP

Frequently asked questions

Can GTX 1070 Ti 8GB run internlm2 math plus 7b IMat?

Yes, GTX 1070 Ti 8GB can run internlm2 math plus 7b IMat with a C grade (Tight fit). Expected decode speed: 35.4 tok/s.

How much VRAM does internlm2 math plus 7b IMat need?

internlm2 math plus 7b IMat (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm2 math plus 7b IMat?

The recommended quantization for internlm2 math plus 7b IMat is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm2 math plus 7b IMat run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, internlm2 math plus 7b IMat achieves approximately 35.4 tokens per second decode speed with a time-to-first-token of 5473ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run internlm2 math plus 7b IMat for coding?

For coding workloads, internlm2 math plus 7b IMat on GTX 1070 Ti 8GB receives a C grade with 35.4 tok/s and 34K context.

What context window can internlm2 math plus 7b IMat use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, internlm2 math plus 7b IMat can safely use up to 34K 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 internlm2 math plus 7b IMat

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