RTX A6000 48GBGünstige Wahl48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.30.6 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $4,650 MSRP
Can Yi 34B Chat run on NVIDIA V100 32GB?
YES — Tight Fit
C52Usable
Estimated from fit model
Yi 34B Chat needs ~28.5 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~32 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: Balanced
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) — 28.5 GB, 31.6 tok/s, Tight fit
28.5 GB required32.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
31.6 tok/s
TTFT
6133 ms
Safe context
31K
Memory
28.5 GB / 32.0 GB
Memory breakdown
Weights20.7 GB
KV Cache3.7 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsYi 34B Chat on NVIDIA V100 32GB
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: 31.6 tok/s decode · 6.1s TTFT (warm) · 79 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 | C | Tight fit | 31.6 tok/s | 3345 ms | 31K |
| Coding | C | Tight fit | 31.6 tok/s | 6133 ms | 31K |
| Agentic Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 26.7 tok/s | 10548 ms | 31K |
| Reasoning | C | Tight fit | 31.6 tok/s | 7248 ms | 31K |
| RAG | C | Runs with offload (needs ~0.1 GB host RAM) | 26.7 tok/s | 13185 ms | 31K |
Quantization options
How Yi 34B Chat (34B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | C49 |
Q3_K_S | 3 | 16.7 GB | Low | C51 |
NVFP4 | 4 | 19.0 GB | Medium | C51 |
Q4_K_M | 4 | 20.7 GB | Medium | C50 |
Q5_K_MBest for your GPU | 5 | 24.5 GB | High | C50 |
Q6_K | 6 | 27.9 GB | High | F0 |
Q8_0 | 8 | 36.4 GB | Very High | F0 |
F16 | 16 | 69.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run Yi 34B Chat on your machine.
Run
lms load Yi-34B-Chat && lms server startUpgrade-Optionen
Hardware, die Yi 34B Chat gut ausführt
RTX A6000 48GBGünstige WahlRTX PRO 5000 Blackwell 48GBBestes Preis-Leistungs-Verhältnis48 GB VRAM (+16)1344 GB/s (+444)
B
Raises estimated decode speed by about 87%.59.1 tok/s Dekodierung
Raises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
ca. $4,999 MSRP
NVIDIA A40 48GBNVIDIA-Upgrade48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.28.4 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $5,500 MSRP
Frequently asked questions
Can NVIDIA V100 32GB run Yi 34B Chat?
Yes, NVIDIA V100 32GB can run Yi 34B Chat with a C grade (Tight fit). Expected decode speed: 31.6 tok/s.
How much VRAM does Yi 34B Chat need?
Yi 34B Chat (34B parameters) requires approximately 28.5 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 34B Chat?
The recommended quantization for Yi 34B Chat is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 34B Chat run at on NVIDIA V100 32GB?
On NVIDIA V100 32GB, Yi 34B Chat achieves approximately 31.6 tokens per second decode speed with a time-to-first-token of 6133ms using Q4_K_M quantization.
Can NVIDIA V100 32GB run Yi 34B Chat for coding?
For coding workloads, Yi 34B Chat on NVIDIA V100 32GB receives a C grade with 31.6 tok/s and 31K context.
What context window can Yi 34B Chat use on NVIDIA V100 32GB?
On NVIDIA V100 32GB, Yi 34B Chat can safely use up to 31K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.
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