RTX 3060 12GB经济之选12 GB VRAM (+4)360 GB/s (+88)
C
Adds memory headroom for longer context windows and future model growth.55.6 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can internlm2 math plus 7b IMat run on RTX 4060 8GB?
YES — Tight Fit
C51Usable
Estimated from fit model
internlm2 math plus 7b IMat needs ~7.1 GB VRAM. RTX 4060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~47 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.
Select quantization to explore
Q4_K_M (Medium quality) — 7.1 GB, 46.5 tok/s, Tight fit
7.1 GB required8.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
46.5 tok/s
TTFT
4163 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 4060 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: 46.5 tok/s decode · 4.2s TTFT (warm) · 116 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 | Tight fit | 46.5 tok/s | 2271 ms | 34K |
| Coding | C | Tight fit | 46.5 tok/s | 4163 ms | 34K |
| Agentic Coding | C | Runs with offload | 46.5 tok/s | 6056 ms | 34K |
| Reasoning | C | Tight fit | 46.5 tok/s | 4920 ms | 34K |
| RAG | C | Runs with offload | 46.5 tok/s | 7570 ms | 34K |
Quantization options
How internlm2 math plus 7b IMat (7B params) fits at each quantization level on RTX 4060 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升级选项
能流畅运行 internlm2 math plus 7b IMat 的硬件
RTX 3060 12GB经济之选RTX 5070 12GB最佳性价比12 GB VRAM (+4)672 GB/s (+400)
C
Raises estimated decode speed by about 111%.98 tok/s 解码
Raises estimated decode speed by about 111%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBNVIDIA 升级12 GB VRAM (+4)504 GB/s (+232)
C
Raises estimated decode speed by about 95%.90.9 tok/s 解码
Raises estimated decode speed by about 95%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Frequently asked questions
Can RTX 4060 8GB run internlm2 math plus 7b IMat?
Yes, RTX 4060 8GB can run internlm2 math plus 7b IMat with a C grade (Tight fit). Expected decode speed: 46.5 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 4060 8GB?
On RTX 4060 8GB, internlm2 math plus 7b IMat achieves approximately 46.5 tokens per second decode speed with a time-to-first-token of 4163ms using Q4_K_M quantization.
Can RTX 4060 8GB run internlm2 math plus 7b IMat for coding?
For coding workloads, internlm2 math plus 7b IMat on RTX 4060 8GB receives a C grade with 46.5 tok/s and 34K context.
What context window can internlm2 math plus 7b IMat use on RTX 4060 8GB?
On RTX 4060 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.
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