Can internlm2 math plus 7b IMat run on RX 7600 XT 16GB?

YES — Runs Great

C49Usable

Estimated from fit model

internlm2 math plus 7b IMat needs ~7.6 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~39 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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) — 7.6 GB, 39.1 tok/s, Runs well

7.6 GB required16.0 GB available

48% VRAM used

Fit status

Runs well

Decode

39.1 tok/s

TTFT

4949 ms

Safe context

180K

Memory

7.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm2 math plus 7b IMat on RX 7600 XT 16GB
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: 39.1 tok/s decode · 4.9s TTFT (warm) · 98 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	39.1 tok/s	2699 ms	180K
Coding	C	Runs well	39.1 tok/s	4949 ms	180K
Agentic Coding	C	Runs well	39.1 tok/s	7198 ms	180K
Reasoning	C	Runs well	39.1 tok/s	5849 ms	180K
RAG	C	Runs well	39.1 tok/s	8998 ms	180K

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 RX 7600 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C46
Q3_K_S	3	3.4 GB	Low	C47
NVFP4	4	3.9 GB	Medium	C47
Q4_K_M	4	4.3 GB	Medium	C48
Q5_K_M	5	5.0 GB	High	C48
Q6_K	6	5.7 GB	High	C49
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

Opciones de mejora

Hardware que ejecuta bien internlm2 math plus 7b IMat

RX 7900 XT 20GBOpción económica

20 GB VRAM (+4)800 GB/s (+512)

Sube la velocidad estimada de decodificación alrededor de un 151%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 151%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$899 MSRP

RX 7900 XTX 24GBMejor relación calidad-precio

24 GB VRAM (+8)960 GB/s (+672)

Sube la velocidad estimada de decodificación alrededor de un 151%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 151%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$999 MSRP

Frequently asked questions

Can RX 7600 XT 16GB run internlm2 math plus 7b IMat?

Yes, RX 7600 XT 16GB can run internlm2 math plus 7b IMat with a C grade (Runs well). Expected decode speed: 39.1 tok/s.

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

internlm2 math plus 7b IMat (7B parameters) requires approximately 7.6 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 RX 7600 XT 16GB?

On RX 7600 XT 16GB, internlm2 math plus 7b IMat achieves approximately 39.1 tokens per second decode speed with a time-to-first-token of 4949ms using Q4_K_M quantization.

Can RX 7600 XT 16GB run internlm2 math plus 7b IMat for coding?

For coding workloads, internlm2 math plus 7b IMat on RX 7600 XT 16GB receives a C grade with 39.1 tok/s and 180K context.

What context window can internlm2 math plus 7b IMat use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, internlm2 math plus 7b IMat can safely use up to 180K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7600 XT 16GB See all hardware for internlm2 math plus 7b IMat

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