RX 7900 XT 20GBBest value
20 GB VRAM (+4)800 GB/s (+480)
B
Raises estimated decode speed by about 131%.95.1 tok/s decode
Raises estimated decode speed by about 131%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Yi 1.5 9B needs ~9.8 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~41 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
41.2 tok/s
TTFT
4699 ms
Safe context
4K
Memory
9.8 GB / 16.0 GB
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 41.2 tok/s | 2563 ms | 4K |
| Coding | B | Runs well | 41.2 tok/s | 4699 ms | 4K |
| Agentic Coding | B | Runs well | 41.2 tok/s | 6835 ms | 4K |
| Reasoning | B | Runs well | 41.2 tok/s | 5553 ms | 4K |
| RAG | B | Runs well | 41.2 tok/s | 8543 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Yi 1.5 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 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 | 126.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 121.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 110.3 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 91.9 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 87.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 74.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 74.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 74.3 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 47.5 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 47.1 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 43.6 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 38.3 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 23.3 | Heavy offload |
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 1.5 9B (9B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C52 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4 | 4 | 5.0 GB | Medium | C53 |
Q4_K_M | 4 | 5.5 GB | Medium | C54 |
Q5_K_M | 5 | 6.5 GB | High | C55 |
Q6_K | 6 | 7.4 GB | High | B56 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | B56 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Copy-paste commands to run Yi 1.5 9B on your machine.
Run
lms load Yi-1.5-9B-Chat && lms server startUpgrade options
Raises estimated decode speed by about 131%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
RTX A4500 20GBBudget pickRaises estimated decode speed by about 140%.
Adds memory headroom for longer context windows and future model growth.
~$2,000 MSRP
Yes, NVIDIA T4 16GB can run Yi 1.5 9B with a B grade (Runs well). Expected decode speed: 41.2 tok/s.
Yi 1.5 9B (9B parameters) requires approximately 9.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA T4 16GB, Yi 1.5 9B achieves approximately 41.2 tokens per second decode speed with a time-to-first-token of 4699ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 9B on NVIDIA T4 16GB receives a B grade with 41.2 tok/s and 4K context.
On NVIDIA T4 16GB, Yi 1.5 9B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/yi-1.5-9b-on-t4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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