24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 133%.53.7 tok/s decode
Raises estimated decode speed by about 133%.
~$1,499 MSRP
Can internlm2 math plus 20b i1 run on RTX 4000 Ada 20GB?
YES — Tight Fit
C49Usable
○Estimated from fit model
internlm2 math plus 20b i1 needs ~17.7 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~23 tok/s.
Runtime: OllamaCapacity: TightBandwidth: 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) — 17.7 GB, 23.0 tok/s, Tight fit
17.7 GB required20.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
23.0 tok/s
TTFT
8411 ms
Safe context
31K
Memory
17.7 GB / 20.0 GB
Memory breakdown
Weights12.2 GB
KV Cache2.3 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsinternlm2 math plus 20b i1 on RTX 4000 Ada 20GB
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: 23.0 tok/s decode · 8.4s TTFT (warm) · 58 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 | 23.0 tok/s | 4588 ms | 31K |
| Coding | C | Tight fit | 23.0 tok/s | 8411 ms | 31K |
| Agentic Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 17.1 tok/s | 16464 ms | 31K |
| Reasoning | C | Tight fit | 23.0 tok/s | 9941 ms | 31K |
| RAG | C | Runs with offload (needs ~0.1 GB host RAM) | 17.1 tok/s | 20580 ms |
Quantization options
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C49 |
Q3_K_S | 3 | 9.8 GB | Low | C50 |
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