48 GB VRAM (+16)
B
Adds memory headroom for longer context windows and future model growth.59.1 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
Can Yi 1.5 34B run on RTX 5090 32GB?
YES — Tight Fit
B65Good
○Estimated from fit model
Yi 1.5 34B needs ~28.8 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~58 tok/s.
Runtime: OllamaCapacity: TightBandwidth: HighStack: 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) — 28.8 GB, 62.9 tok/s, Tight fit
28.8 GB required32.0 GB available
90% VRAM used
Fit status
Tight fit
Decode
62.9 tok/s
TTFT
3080 ms
Safe context
4K
Memory
28.8 GB / 32.0 GB
Memory breakdown
Weights20.7 GB
KV Cache3.7 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsYi 1.5 34B on RTX 5090 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 62.9 tok/s decode · 3.1s TTFT (warm) · 157 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 | B | Tight fit | 62.9 tok/s | 1680 ms | 4K |
| Coding | B | Tight fit | 57.9 tok/s | 3344 ms | 4K |
| Agentic Coding | B | Runs with offload | 43.0 tok/s | 6551 ms | 4K |
| Reasoning | B | Tight fit | 57.9 tok/s | 3952 ms | 4K |
| RAG | B | Runs with offload | 43.0 tok/s | 8188 ms | 4K |
Quantization options
How Yi 1.5 34B (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 | B61 |
Q3_K_S | 3 | 16.7 GB | Low | B62 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Yi 1.5 34B on your machine.
Run
lms load Yi-1.5-34B-Chat && lms server startUpgrade options
Hardware that runs Yi 1.5 34B well
48 GB VRAM (+16)
B
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
40 GB VRAM (+8)
B
Adds memory headroom for longer context windows and future model growth.68.4 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$10,000 MSRP