Meta-Llama-3-70B vs Qwen3-Coder-Next
Meta-Llama-3-70B (70.6B parameters) and Qwen3-Coder-Next (79.7B parameters) side by side: the memory each needs at every precision, what it costs to run on a live GPU, and the context window, KV cache and license where they are published. Numbers are computed from the models' published specs; this page does not rank quality.
Side by side
| Fact | Meta-Llama-3-70B | Qwen3-Coder-Next |
|---|---|---|
| Parameters | 70.6B | 79.7B (Mixture-of-experts: 10 of 512 experts active per token (exact active-parameter count not stated on the model card)) |
| Architecture | Grouped-query attention | Hybrid (some layers use full attention); mixture of 512 experts, 10 active per token |
| Context length | 8,192 tokens | 256K tokens (262,144) |
| License | – | Apache 2.0 |
| Published precision | BF16 | BF16 |
| VRAM needed, As published | 158 GB | 178 GB |
| VRAM needed, FP8 | 78.8 GB | 89.0 GB |
| VRAM needed, INT4 | 39.4 GB | 44.5 GB |
| Cheapest live fit, As published | RTX A5000 × 7 · $1.23/hr | RTX A5000 × 8 · $1.41/hr |
| Cheapest live fit, FP8 | RTX PRO 6000 · $1.38/hr | RTX PRO 6000 · $1.38/hr |
| Cheapest live fit, INT4 | RTX A6000 · $0.363/hr | RTX A6000 · $0.363/hr |
| KV cache per token (16-bit) | 320 KB | 24 KB |
| KV cache at 32k tokens | 10.0 GB | 0.75 GB |
| KV cache at 128k tokens | 40.0 GB | 3.00 GB |
VRAM is the weight size at each precision times a flat 1.2 overhead; see the methodology. The FP8 and INT4 rows need a quantized checkpoint or an engine that quantizes on load. The fit is the lowest-priced single GPU type that holds the model at that precision, or the lowest-priced multi-GPU set (up to 8) when none does. KV cache is for one sequence at 16-bit, computed from each model's config where the attention layout is known.
Which to pick
- Meta-Llama-3-70B needs less VRAM at its published precision (158 GB against 178 GB), so it fits on a smaller GPU.
- Qwen3-Coder-Next lists the longer context window (262,144 tokens against 8,192).
- Qwen3-Coder-Next caches less per sequence at 32k tokens (0.8 GB against 10.0 GB), leaving more memory for batching.
- Meta-Llama-3-70B has the cheaper live GPU fit at its published precision ($1.23/hr against $1.41/hr).
These follow only from the facts in the table above. Whether either model does your task well is a separate question this page does not answer.
Keep reading
- Meta-Llama-3-70B: full VRAM table and live GPU fit
- Qwen3-Coder-Next: full VRAM table and live GPU fit
- The Llama model series
- The Qwen model series
- All models that fit in 192 GB
Other comparisons
