Can internlm2 math plus 7b IMat run on RTX 2070 Super 8GB?

YES — Tight Fit

C52Usable

Estimated from fit model

internlm2 math plus 7b IMat needs ~7.1 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~64 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, 64.0 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

64.0 tok/s

TTFT

3025 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 RTX 2070 Super 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: 64.0 tok/s decode · 3.0s TTFT (warm) · 160 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	64.0 tok/s	1650 ms	34K
Coding	C	Tight fit	64.0 tok/s	3025 ms	34K
Agentic Coding	C	Runs with offload	64.0 tok/s	4400 ms	34K
Reasoning	C	Tight fit	64.0 tok/s	3575 ms	34K
RAG	C	Runs with offload	64.0 tok/s	5500 ms	34K

Quantization options

How internlm2 math plus 7b IMat (7B params) fits at each quantization level on RTX 2070 Super 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

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

Upgrade options

Hardware that runs internlm2 math plus 7b IMat well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.55.6 tok/s decode

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

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+4)672 GB/s (+224)

Raises estimated decode speed by about 53%.98 tok/s decode

Raises estimated decode speed by about 53%.

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

~$549 MSRP

RTX 4070 Super 12GBNVIDIA upgrade

12 GB VRAM (+4)504 GB/s (+56)

Raises estimated decode speed by about 42%.90.9 tok/s decode

Raises estimated decode speed by about 42%.

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

~$599 MSRP

Frequently asked questions

Can RTX 2070 Super 8GB run internlm2 math plus 7b IMat?

Yes, RTX 2070 Super 8GB can run internlm2 math plus 7b IMat with a C grade (Tight fit). Expected decode speed: 64.0 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 RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, internlm2 math plus 7b IMat achieves approximately 64.0 tokens per second decode speed with a time-to-first-token of 3025ms using Q4_K_M quantization.

Can RTX 2070 Super 8GB run internlm2 math plus 7b IMat for coding?

For coding workloads, internlm2 math plus 7b IMat on RTX 2070 Super 8GB receives a C grade with 64.0 tok/s and 34K context.

What context window can internlm2 math plus 7b IMat use on RTX 2070 Super 8GB?

On RTX 2070 Super 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 RTX 2070 Super 8GB See all hardware for internlm2 math plus 7b IMat

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