Yi 34B Chat needs ~27.7 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q4_K_M quantization, expect ~20 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
3.7 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~2.8 GB host RAM)
Decode
20.1 tok/s
TTFT
9649 ms
Safe context
4K
Memory
27.7 GB / 24.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 2.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 21.3 tok/s | 4948 ms | 4K |
| Coding | C | Very compromised (needs ~2.8 GB host RAM) | 20.1 tok/s | 9649 ms | 4K |
| Agentic Coding | F | Too heavy | 15.4 tok/s | 18227 ms | 4K |
| Reasoning | C | Very compromised (needs ~2.8 GB host RAM) | 20.1 tok/s | 11403 ms | 4K |
| RAG | F | Too heavy | 15.4 tok/s | 22784 ms |
How Yi 34B Chat (34B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | C52 |
Q3_K_SBest for your GPU | 3 | 16.7 GB | Low | C51 |
Copy-paste commands to run Yi 34B Chat on your machine.
Run
lms load Yi-34B-Chat && lms server startUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$2,249 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
NVIDIA A100 40GBBiggest leapRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 240%.
~$10,000 MSRP
Yes, RX 7900 XTX 24GB can run Yi 34B Chat with a C grade (Very compromised (needs ~2.8 GB host RAM)). Expected decode speed: 20.1 tok/s.
Yi 34B Chat (34B parameters) requires approximately 27.7 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 RX 7900 XTX 24GB, Yi 34B Chat achieves approximately 20.1 tokens per second decode speed with a time-to-first-token of 9649ms using Q4_K_M quantization.
For coding workloads, Yi 34B Chat on RX 7900 XTX 24GB receives a C grade with 20.1 tok/s and 4K context.
On RX 7900 XTX 24GB, Yi 34B Chat can safely use up to 4K tokens of context. The model's official context limit is 200K, 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-34b-chat-on-rx-7900-xtx-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
19.0 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 20.7 GB | Medium | F0 |
Q5_K_M | 5 | 24.5 GB | High | F0 |
Q6_K | 6 | 27.9 GB | High | F0 |
Q8_0 | 8 | 36.4 GB | Very High | F0 |
F16 | 16 | 69.7 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.