Home / Models / Qwen2.5-Coder 7B Instruct / NVIDIA RTX 4060 Ti 8 GB
Can Qwen2.5-Coder 7B Instruct run on NVIDIA RTX 4060 Ti 8 GB?
Yes. Calculated memory fit: Qwen2.5-Coder 7B Instruct can run entirely in this GPU’s usable VRAM at one or more supported quantizations.
Can it run?
- Can it run?
- Yes, full GPU fit (calculated estimate).
- Full GPU fit?
- Yes
- Model
- Qwen2.5-Coder 7B Instruct · 7.6B
- GPU
- NVIDIA RTX 4060 Ti 8 GB · 8 GB advertised
What else can NVIDIA RTX 4060 Ti 8 GB run? · Find other GPUs for Qwen2.5-Coder 7B Instruct
Memory calculation
Breakdown for Q4 at 8K context, batch size 1. Calculator v1.0.0.
| Model weights | 4.5 GB |
|---|---|
| KV cache | 0.4 GB |
| Runtime reserve | 0.7 GB |
| Safety margin | 0.4 GB |
| Required (estimated) | 6.0 GB |
| GPU usable VRAM | 7.2 GB |
Result: FITS IN VRAM
Quantization table
| Quantization | 2K | 4K | 8K | 16K | 32K |
|---|---|---|---|---|---|
| BF16 | Offload | Offload | Offload | Offload | Offload |
| FP16 | Offload | Offload | Offload | Offload | Offload |
| Q3 | Fits | Fits | Fits | Fits | Fits |
| Q4 | Fits | Fits | Fits | Fits | Limited context |
| Q5 | Fits | Fits | Fits | Limited context | Limited context |
| Q6 | Offload | Offload | Offload | Offload | Offload |
| Q8 | Offload | Offload | Offload | Offload | Offload |
What fits entirely in VRAM?
- Q3 at 16K context
- Q3 at 2K context
- Q3 at 32K context
- Q3 at 4K context
- Q3 at 8K context
- Q4 at 16K context
- Q4 at 2K context
- Q4 at 4K context
- Q4 at 8K context
- Q5 at 2K context
- Q5 at 4K context
- Q5 at 8K context
What requires RAM offload?
Offload is shown only when the model does not fully fit in usable VRAM but stays within the modeled offload allowance.
Performance
Separate from memory fit. Runtime is llama.cpp · quantization bucket Q4 (GGUF Q4_0 maps to Q4). Workload is llama-bench pp512 / tg128 (short context, batch 1, full GPU offload). This is not a ranking and not a 8K–128K speed claim.
- Model
- Qwen2.5-Coder 7B Instruct
- GPU
- NVIDIA RTX 4060 Ti 8 GB
- Runtime
- llama.cpp · CUDA · model 1.0.0
- Decode
- ~51 tok/s · Calibrated estimate · HIGH confidence (spread ~45 tok/s–~57 tok/s)
- Prefill
- ~3364 tok/s at a 512-token prompt · Calibrated estimate · MEDIUM confidence
- Time to first token
- ~152 ms for a 512-token prompt (512 / prefill tok/s). Not estimated at 8K–128K.
decode_tps = GPU_efficiency × memory_bandwidth / model_weight_bytes; efficiency from same-gpu Llama 2 7B Q4_0 efficiency
Available graphics cards
These SKUs use this GPU chip. Amazon CTAs appear only for EXACT/HIGH matches. Absence of a price does not change the compatibility result above.
MSI VENTUS GeForce RTX 4060 Ti 2X BLACK 8G NVIDIA 8 GB GDDR6
8 GB · Amazon EXACT
Check price on Amazon
ASUS Dual -RTX4060TI-O8G NVIDIA GeForce RTX 4060 Ti 8 GB GDDR6
8 GB · Amazon EXACT
Check price on Amazon
GIGABYTE EAGLE GeForce RTX 4060 Ti OC 8G NVIDIA 8 GB GDDR6
8 GB · Amazon EXACT
Check price on Amazon
ASUS Dual -RTX4060TI-O8G-WHITE NVIDIA GeForce RTX 4060 Ti 8 GB GDDR6
8 GB · Amazon EXACT
Check price on Amazon
GIGABYTE EAGLE GeForce RTX 4060 Ti 8G NVIDIA 8 GB GDDR6
8 GB · Amazon EXACT
Check price on Amazon
GIGABYTE AERO GeForce RTX 4060 Ti OC 8G NVIDIA 8 GB GDDR6
8 GB · Amazon EXACT
Check price on Amazon
MSI VENTUS GeForce RTX 4060 Ti 2X BLACK 8G OC NVIDIA 8 GB GDDR6
8 GB · Amazon EXACT
Check price on Amazon
ASUS TUF Gaming TUF-RTX4060TI-O8G-GAMING NVIDIA GeForce RTX 4060 Ti 8 GB GDDR6
8 GB · Amazon unmatched
GIGABYTE GAMING GeForce RTX 4060 Ti OC 8G NVIDIA GeForce RTX 4060 Ti 8 GB GDDR6
8 GB · Amazon unmatched
GIGABYTE GeForce RTX 4060 Ti WINDFORCE OC NVIDIA 8 GB GDDR6
8 GB · Amazon unmatched
ASUS Dual -RTX4060TI-O8G-EVO NVIDIA GeForce RTX 4060 Ti 8 GB GDDR6
8 GB · Amazon unmatched
GIGABYTE AORUS GeForce RTX 4060 Ti ELITE 8G NVIDIA 8 GB GDDR6
8 GB · Amazon unmatched
MSI GAMING GeForce RTX 4060 Ti X SLIM 8G NVIDIA 8 GB GDDR6
8 GB · Amazon unmatched
MSI GAMING GeForce RTX 4060 Ti X TRIO 8G NVIDIA 8 GB GDDR6
8 GB · Amazon unmatched
Sources
Model: official config Qwen/Qwen2.5-Coder-7B-Instruct (main). GPU/product specs: Icecat. Compatibility: RigForAI calculator v1.0.0 estimated VRAM · Q8 at 8K: CPU_RAM_OFFLOAD_REQUIRED. Amazon: affiliate commerce match, not a spec source.