Can internlm2 math plus 7b IMat run on GTX 1080 Ti 11GB?

YES — Runs Great

C55Usable

Estimated from fit model

internlm2 math plus 7b IMat needs ~7.4 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~67 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 7.4 GB, 66.9 tok/s, Runs well

7.4 GB required11.0 GB available

67% VRAM used

Fit status

Runs well

Decode

66.9 tok/s

TTFT

2895 ms

Safe context

86K

Memory

7.4 GB / 11.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsinternlm2 math plus 7b IMat on GTX 1080 Ti 11GB
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: 66.9 tok/s decode · 2.9s TTFT (warm) · 167 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	Runs well	66.9 tok/s	1579 ms	86K
Coding	C	Runs well	66.9 tok/s	2895 ms	86K
Agentic Coding	B	Runs well	66.9 tok/s	4211 ms	86K
Reasoning	C	Runs well	66.9 tok/s	3421 ms	86K
RAG	B	Runs well	66.9 tok/s	5263 ms	86K

Quantization options

How internlm2 math plus 7b IMat (7B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C49
Q3_K_S	3	3.4 GB	Low	C50
NVFP4	4	3.9 GB	Medium	C51
Q4_K_M	4	4.3 GB	Medium	C52
Q5_K_M	5	5.0 GB	High	C52
Q6_K	6	5.7 GB	High	C52
Q8_0Best for your GPU	8	7.5 GB	Very High	C51
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

Frequently asked questions

Can GTX 1080 Ti 11GB run internlm2 math plus 7b IMat?

Yes, GTX 1080 Ti 11GB can run internlm2 math plus 7b IMat with a C grade (Runs well). Expected decode speed: 66.9 tok/s.

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

internlm2 math plus 7b IMat (7B parameters) requires approximately 7.4 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 1080 Ti 11GB?

On GTX 1080 Ti 11GB, internlm2 math plus 7b IMat achieves approximately 66.9 tokens per second decode speed with a time-to-first-token of 2895ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run internlm2 math plus 7b IMat for coding?

For coding workloads, internlm2 math plus 7b IMat on GTX 1080 Ti 11GB receives a C grade with 66.9 tok/s and 86K context.

What context window can internlm2 math plus 7b IMat use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, internlm2 math plus 7b IMat can safely use up to 86K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1080 Ti 11GB See all hardware for internlm2 math plus 7b IMat

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