32 GB VRAM (+8)1792 GB/s (+1492)
C
Raises estimated decode speed by about 515%.98.4 tok/s decode
Raises estimated decode speed by about 515%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can internlm2 math plus 20b i1 run on NVIDIA L4 24GB?
YES — Runs Great
C51Usable
○Estimated from fit model
internlm2 math plus 20b i1 needs ~18.1 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~16 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
Share:
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) — 18.1 GB, 16.0 tok/s, Runs well
18.1 GB required24.0 GB available
75% VRAM used
Fit status
Runs well
Decode
16.0 tok/s
TTFT
12112 ms
Safe context
56K
Memory
18.1 GB / 24.0 GB
Memory breakdown
Weights12.2 GB
KV Cache2.3 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsinternlm2 math plus 20b i1 on NVIDIA L4 24GB
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: 16.0 tok/s decode · 12.1s TTFT (warm) · 40 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 | 16.0 tok/s | 6607 ms | 56K |
| Coding | C | Runs well | 16.0 tok/s | 12112 ms | 56K |
| Agentic Coding | C | Tight fit | 16.0 tok/s | 17618 ms | 56K |
| Reasoning | C | Runs well | 16.0 tok/s | 14315 ms | 56K |
| RAG | C | Tight fit | 16.0 tok/s | 22023 ms | 56K |
Quantization options
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C47 |
Q3_K_S | 3 | 9.8 GB | Low | C48 |
NVFP4 | 4 |
Get started
Copy-paste commands to run internlm2 math plus 20b i1 on your machine.
Run
lms load hf-mradermacher--internlm2-math-plus-20b-i1-gguf && lms server startUpgrade options
Hardware that runs internlm2 math plus 20b i1 well
32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 286%.61.7 tok/s decode
Raises estimated decode speed by about 286%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
32 GB VRAM (+8)576 GB/s (+276)
C
Raises estimated decode speed by about 136%.37.8 tok/s decode
Raises estimated decode speed by about 136%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP