GeForce RTX 4060 Ti 16 GB
NVIDIA
ExcellentRuns Qwen2.5 14B Instruct Q4_K_M entirely in memory
Model · Alibaba Qwen
Qwen2.5 14B Instruct: VRAM and hardware requirements
At Q4_K_M with an 8K context, Qwen2.5 14B Instruct needs about 11.2 GB: a GPU with 12 GB or more runs it entirely in VRAM.
- Parameters14.7B
- Max context128K
- LicenseApache-2.0
Specifications
- Organization
- Alibaba Qwen
- Architecture
- qwen2
- Released
- September 2024
- Layers
- 48
- Hidden size
- 5120
- Attention heads
- 40
- KV heads
- 8
- Head dimension
- 128
- Vocabulary
- 152,064
- KV cache per 1K tokens (f16)
- 0.188 GB
- Repository
- Qwen/Qwen2.5-14B-Instruct
Official specData updated 24 days agoSource: Hugging Face config.json + model card
Quantizations and memory
| Quantization | File size | Memory needed | Minimum GPU memory | Quality |
|---|---|---|---|---|
| BF16 | Not available | ~31.2 GB | 48 GB | Reference (unquantized) |
| Q8_0 | 14.6 GB | ~17.7 GB | 20 GB | Near-lossless |
| Q6_K | 11.3 GB | ~14.2 GB | 16 GB | Near-lossless |
| Q5_K_M | 9.8 GB | ~12.7 GB | 16 GB | Very good |
| Q4_K_MMost used | 8.4 GB | ~11.2 GB | 12 GB | Good — the usual choice |
| IQ4_XS | 7.6 GB | ~10.4 GB | 11 GB | Good — the usual choice |
| Q3_K_M | 6.8 GB | ~9.6 GB | 10 GB | Noticeable loss |
| Q2_K | 5.4 GB | ~8.1 GB | 10 GB | Significant loss |
File sizes: not available until imported from the model repository. Memory = weights (estimated from bits per weight) + KV cache + runtime overhead + safety margin. (BF16 ≈ 16 bpw, Q8_0 ≈ 8.5 bpw, Q6_K ≈ 6.57 bpw, Q5_K_M ≈ 5.7 bpw, Q4_K_M ≈ 4.9 bpw, IQ4_XS ≈ 4.46 bpw, Q3_K_M ≈ 4 bpw, Q2_K ≈ 3.17 bpw)
Q4, Q5 or Q8: how to choose a quantization
GPUs that run Qwen2.5 14B Instruct
| RTX 509032 GB · 64 GB RAM~11.2 GB · 101–154 tok/s estimate | Excellent |
|---|---|
| RTX 409024 GB · 64 GB RAM~11.2 GB · 65–93 tok/s estimate | Excellent |
| RTX 309024 GB · 64 GB RAM~11.2 GB · 61–87 tok/s estimate | Excellent |
| RX 7900 XTX24 GB · 64 GB RAM~11.3 GB · 52–79 tok/s estimate | Excellent |
| RTX 5060 Ti 16 GB16 GB · 64 GB RAM~11.2 GB · 32–43 tok/s estimate | Excellent |
| RTX 5070 Ti16 GB · 64 GB RAM~11.2 GB · 59–83 tok/s estimate | Excellent |
| RTX 508016 GB · 64 GB RAM~11.2 GB · 62–89 tok/s estimate | Excellent |
| RTX 4060 Ti 16 GB16 GB · 64 GB RAM~11.2 GB · 21–28 tok/s estimate | Excellent |
| RTX 4070 Ti SUPER16 GB · 64 GB RAM~11.2 GB · 46–64 tok/s estimate | Excellent |
| RX 9070 XT16 GB · 64 GB RAM~11.3 GB · 38–55 tok/s estimate | Excellent |
| RTX 3060 12 GB12 GB · 64 GB RAM~11.2 GB · 26–35 tok/s estimate | Compatible with limits |
| RTX 407012 GB · 64 GB RAM~11.2 GB · 36–48 tok/s estimate | Compatible with limits |
| RTX 507012 GB · 64 GB RAM~11.2 GB · 46–64 tok/s estimate | Compatible with limits |
| RTX 40608 GB · 64 GB RAM~11.2 GB · 5–9.5 tok/s estimate | CPU/GPU offload |
Macs that run Qwen2.5 14B Instruct
| M3 Ultra (80-core GPU) · 512 GBMac Studio~11 GB · 44–77 tok/s estimate | Excellent |
|---|---|
| M4 Max (40-core GPU) · 128 GBMacBook Pro 14, MacBook Pro 16~11 GB · 32–51 tok/s estimate | Excellent |
| M3 Max (40-core GPU) · 128 GBMacBook Pro 14, MacBook Pro 16~11 GB · 24–38 tok/s estimate | Excellent |
| M4 Pro (20-core GPU) · 64 GBMac mini, MacBook Pro 14~11 GB · 17–26 tok/s estimate | Excellent |
| M4 Max (32-core GPU) · 36 GBMacBook Pro 14, MacBook Pro 16~11 GB · 25–39 tok/s estimate | Excellent |
| M5 · 32 GBMacBook Pro 14~11 GB · 10–15 tok/s estimate | Excellent |
| M4 · 32 GBMacBook Air, MacBook Pro 14~11 GB · 8–12 tok/s estimate | Compatible |
| M3 · 24 GBMacBook Air, MacBook Pro 14~11 GB · 6.5–9.5 tok/s estimate | Compatible |
EstimateData updated 24 days agoSource: Run LLM Locally engine
Limits in plain words
The KV cache grows with context: at 32K it adds about 6 GB on top of the weights.
With under 8 GB of VRAM, use a smaller quantization or a smaller model of the same family.
Where to buy
Prices and links follow the market you choose.
Radeon RX 7600 XT
AMD
ExcellentRuns Qwen2.5 14B Instruct Q4_K_M entirely in memory
MacBook Pro 14 M3 Pro
Apple Silicon
CompatibleRuns Qwen2.5 14B Instruct Q4_K_M entirely in memory
AMD Ryzen AI Max+ 395
Unified memory PC
ExcellentRuns Qwen2.5 14B Instruct Q4_K_M entirely in memory
No current offer imported for this market: the link opens a marketplace search.
Frequently asked questions
How much VRAM does Qwen2.5 14B Instruct need?
About 11.2 GB at Q4_K_M with an 8K context, including the KV cache and runtime overhead. At Q8_0 it needs about 17.7 GB. A longer context adds more: about 6 GB of KV cache at 32K.
Can Qwen2.5 14B Instruct run on a Mac?
Yes on a Mac whose GPU can use at least 11 GB, e.g. M3 24 GB. macOS lets the GPU use about two thirds to three quarters of unified memory by default.
How fast is Qwen2.5 14B Instruct?
On a RTX 4090, we estimate 65–93 tok/s at Q4_K_M. This is an estimate from memory bandwidth (confidence low) until benchmarks are imported.