This GPU offers reliable throughput for local AI workloads. Pair it with the right model quantization to hit your desired tokens/sec, and monitor prices below to catch the best deal.
Quick Answer: NVIDIA A5000 has 24GB VRAM, enough for models up to roughly 60B parameters at 4-bit quantization. It draws 230W under load.
With 24GB VRAM, NVIDIA A5000 can run models up to approximately 60B parameters using 4-bit quantization. That covers most popular models, including 70B-class ones at 4-bit.
Consider RTX 4090 or RTX 6000 Ada — 24GB Ada offers better efficiency than Ampere.
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Data-backed answers pulled from community benchmarks, manufacturer specs, and live pricing.
RunPod benchmarks show the 24 GB RTX A5000 pushing ~49 tokens/sec on Mixtral 8x7B Q2_K under Ollama, and about 38 tok/s at Q3_K_S.
Source: Reddit – /r/LocalLLaMA (19428v9)
Yes—with low-bit EXL2 quantization. Community guides note that 2.4 bpw EXL2 plus 4-bit KV cache lets Miqu 70B run entirely within 24 GB on cards like the A5000.
Source: Reddit – /r/LocalLLaMA (kx452no)
Operators of quad-A5000 rigs suggest disabling NVLink peer-to-peer via NCCL env flags when vLLM underperforms—removing the bridges boosted throughput from ~14 tok/s to ~25 tok/s.
Source: Reddit – /r/LocalLLaMA (n3vnbez)
RTX A5000 is rated at 230 W, uses a single 8-pin connector, and NVIDIA recommends a 600 W PSU.
Source: TechPowerUp – RTX A5000 Specs
Showing 12 of 80 rows. Speeds are calculated estimates, not measurements — search for your model to jump straight to it.
| Model | Size | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|---|
| Deepseek AI Deepseek Coder 1.3B Instruct | 1.3B | Q4 | ~190 tok/sEstimated | 1GB |
| Deepseek AI Deepseek R1 Distill Qwen 1.5B | 1.5B | Q4 | ~190 tok/sEstimated | 1GB |
| Deepseek AI Deepseek Ocr 2 | Unknown | Q4 | ~155 tok/sEstimated | 2GB |
| Deepseek AI Deepseek Ocr | Unknown | Q4 | ~155 tok/sEstimated | 2GB |
| Lmstudio Community Deepseek R1 0528 Qwen3 8B Mlx 8bit | 8B | Q4 | ~155 tok/sEstimated | 4GB |
| Lmstudio Community Deepseek R1 0528 Qwen3 8B Mlx 4bit | 8B | Q4 | ~155 tok/sEstimated | 4GB |
| Deepseek AI Deepseek R1 Distill Qwen 7B | 7B | Q4 | ~155 tok/sEstimated | 4GB |
| Nineninesix Kani Tts 2 En | Unknown | Q4 | ~150 tok/sEstimated | 1GB |
| Qwen Qwen3 Tts 12hz 1 7B Customvoice | 7B | Q4 | ~150 tok/sEstimated | 1GB |
| Zai Org Glm Ocr | Unknown | Q4 | ~150 tok/sEstimated | 1GB |
| Qwen Qwen3 Asr 1 7B | 7B | Q4 | ~150 tok/sEstimated | 2GB |
| Nari Labs Dia2 2B | 2B | Q4 | ~150 tok/sEstimated | 1GB |
Showing 12 of 240 rows.
| Model | Size | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|---|
| 01 AI Yi 1 5 34B Chat | 34B | Q4 | Fits comfortably | ~44 tok/sEstimated | 18GB (have 24GB) |
| 01 AI Yi 1 5 34B Chat | 34B | Q8 | Not supported | ~31 tok/sEstimated | 35GB (have 24GB) |
| 01 AI Yi 1 5 34B Chat | 34B | FP16 | Not supported | ~17 tok/sEstimated | 69GB (have 24GB) |
| AI Forever Rugpt 3.5 13B | 13B | Q4 | Fits comfortably | ~94 tok/sEstimated | 7GB (have 24GB) |
| AI Forever Rugpt 3.5 13B | 13B | Q8 | Fits comfortably | ~66 tok/sEstimated | 13GB (have 24GB) |
| AI Forever Rugpt 3.5 13B | 13B | FP16 | Not supported | ~36 tok/sEstimated | 26GB (have 24GB) |
| AI Mo Kimina Prover 72B | 72B | Q4 | Not supported | ~25 tok/sEstimated | 37GB (have 24GB) |
| AI Mo Kimina Prover 72B | 72B | Q8 | Not supported | ~18 tok/sEstimated | 73GB (have 24GB) |
| AI Mo Kimina Prover 72B | 72B | FP16 | Not supported | ~9.5 tok/sEstimated | 146GB (have 24GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | Q4 | Fits comfortably | ~150 tok/sEstimated | 1GB (have 24GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | Q8 | Fits comfortably | ~105 tok/sEstimated | 2GB (have 24GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | FP16 | Fits comfortably | ~57 tok/sEstimated | 4GB (have 24GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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