24 GB VRAM (+4)936 GB/s (+296)
D
Makes the model fit on the accelerator instead of staying completely out of reach.11.8 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
~$1,499 MSRP
Can Yi 34B Chat run on RTX A4500 20GB?
YES — With Q3_K_S
D40Poor
○Estimated from fit model
Yi 34B Chat needs ~23.2 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q3_K_S quantization, expect ~17 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
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.
Yi 34B Chat at Q4_K_M needs 27.3 GB — too much for RTX A4500 20GB (20.0 GB). Runs at Q3_K_S (23.2 GB) with low quality. 2 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 27.3 GB, exceeds 20.0 GB available
27.3 GB required20.0 GB available
137% VRAM needed
7.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
10.2 tok/s
TTFT
19019 ms
Safe context
4K
Memory
27.3 GB / 20.0 GB
Offload
30%
Memory breakdown
Weights20.7 GB
KV Cache3.7 GB
Runtime0.9 GB
Headroom2.0 GB
See how fast it feels
With memory offload — actual speed may be lowerSee how fast it feelsYi 34B Chat on RTX A4500 20GB
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: 10.2 tok/s decode · 19.0s TTFT (warm) · 25 tok/s prefill
What limits this setup
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.
Best improvement path
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 2.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 11.8 tok/s | 8964 ms | 4K |
| Coding | F | Too heavy | 9.4 tok/s | 20649 ms | 4K |
| Agentic Coding | F | Too heavy | 7.8 tok/s | 36056 ms | 4K |
| Reasoning | F | Too heavy | 10.2 tok/s | 22477 ms | 4K |
| RAG | F | Too heavy | 7.8 tok/s | 45071 ms | 4K |
Quantization options
How Yi 34B Chat (34B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 13.3 GB | Low | C52 |
Q3_K_S | 3 | 16.7 GB | Low | F0 |
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
32 GB VRAM (+12)1792 GB/s (+1152)
C
Makes the model fit on the accelerator instead of staying completely out of reach.40.7 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,999 MSRP
24 GB VRAM (+4)768 GB/s (+128)
C
Makes the model fit on the accelerator instead of staying completely out of reach.17.4 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 71%.
~$3,200 MSRP