Yi 34B Chat needs ~30.1 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~19 tok/s.
Operating mode
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
Fit status
Runs well
Decode
19.0 tok/s
TTFT
10180 ms
Safe context
94K
Memory
30.1 GB / 48.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 19.0 tok/s | 5553 ms | 94K |
| Coding | C | Runs well | 19.0 tok/s | 10180 ms | 94K |
| Agentic Coding | C | Runs well | 19.0 tok/s | 14807 ms | 94K |
| Reasoning | C | Runs well | 19.0 tok/s | 12031 ms | 94K |
| RAG | C | Runs well | 19.0 tok/s | 18509 ms | 94K |
Inference speed
Estimated decode speed (tokens/sec) for Yi 34B Chat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~41 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 40.7 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 31.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 31.4 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 29.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 24.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 23.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 20.1 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 19.8 | Tight |
| RTX 4090 24GB | 24 GB | Q4_K_M | 12.8 | Heavy offload |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 12.6 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 11.8 | Heavy offload |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 11.5 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 4.6 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Yi 34B Chat (34B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | C46 |
Q3_K_S | 3 | 16.7 GB | Low | C47 |
NVFP4 | 4 | 19.0 GB | Medium | C47 |
Q4_K_M | 4 | 20.7 GB | Medium | C48 |
Q5_K_M | 5 | 24.5 GB | High | C49 |
Q6_K | 6 | 27.9 GB | High | C50 |
Q8_0Best for your GPU | 8 | 36.4 GB | Very High | C49 |
F16 | 16 | 69.7 GB | Maximum | F0 |
Copy-paste commands to run Yi 34B Chat on your machine.
Run
lms load Yi-34B-Chat && lms server startUpgrade options
NVIDIA A100 80GBBudget pickRaises estimated decode speed by about 372%.
Adds memory headroom for longer context windows and future model growth.
~$15,000 MSRP
NVIDIA A800 80GBBest valueRaises estimated decode speed by about 316%.
Adds memory headroom for longer context windows and future model growth.
~$15,000 MSRP
NVIDIA H800 80GBNVIDIA upgradeRaises estimated decode speed by about 569%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA L20 48GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 19.0 tok/s.
Yi 34B Chat (34B parameters) requires approximately 30.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 34B Chat is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA L20 48GB, Yi 34B Chat achieves approximately 19.0 tokens per second decode speed with a time-to-first-token of 10180ms using Q4_K_M quantization.
For coding workloads, Yi 34B Chat on NVIDIA L20 48GB receives a C grade with 19.0 tok/s and 94K context.
On NVIDIA L20 48GB, Yi 34B Chat can safely use up to 94K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.
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