Home / Models / Qwen2.5-Coder 7B Instruct / NVIDIA GTX 1070 Ti 8 GB
Can Qwen2.5-Coder 7B Instruct run on NVIDIA GTX 1070 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 GTX 1070 Ti 8 GB · 8 GB advertised
What else can NVIDIA GTX 1070 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 GTX 1070 Ti 8 GB
- Runtime
- llama.cpp · CUDA · model 1.0.0
- Decode
- ~30 tok/s · Calibrated estimate · HIGH confidence (spread ~27 tok/s–~34 tok/s)
- Prefill
- ~699 tok/s at a 512-token prompt · Calibrated estimate · MEDIUM confidence
- Time to first token
- ~732 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.
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.