48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.30.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$4,650 MSRP
Can Yi 34B Chat run on RTX 5090 32GB?
YES — Tight Fit
C54Usable
○Estimated from fit model
Yi 34B Chat needs ~28.5 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~58 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: 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) — 28.5 GB, 40.7 tok/s, Tight fit
28.5 GB required32.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
40.7 tok/s
TTFT
4753 ms
Safe context
31K
Memory
28.5 GB / 32.0 GB
Memory breakdown
Weights20.7 GB
KV Cache3.7 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsYi 34B Chat on RTX 5090 32GB
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: 40.7 tok/s decode · 4.8s TTFT (warm) · 102 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 | 57.9 tok/s | 1824 ms | 31K |
| Coding | C | Tight fit | 57.9 tok/s | 3344 ms | 31K |
| Agentic Coding | C | Runs with offload | 43.8 tok/s | 6428 ms | 31K |
| Reasoning | C | Tight fit | 57.9 tok/s | 3952 ms | 31K |
| RAG | C | Runs with offload | 43.8 tok/s | 8035 ms | 31K |
Quantization options
How Yi 34B Chat (34B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | C49 |
Q3_K_S | 3 | 16.7 GB | Low | C51 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Yi 34B Chat on your machine.
Run
lms load Yi-34B-Chat && lms server startUpgrade options
Hardware that runs Yi 34B Chat well
48 GB VRAM (+16)
B
Raises estimated decode speed by about 45%.59.1 tok/s decode
Raises estimated decode speed by about 45%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.41.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,800 MSRP