Model memory guides / meta-llama/Llama-3.3-70B-Instruct
meta-llama/Llama-3.3-70B-Instruct VRAM estimates
For meta-llama/Llama-3.3-70B-Instruct, compare how precision and context change memory planning for text generation.
What changes the memory requirement?
In the Q4_K_M scenario (4.85 effective bits/weight), reported parameters account for approximately 39.84 GiB of weights. At 2,048 tokens, cache and recurrent state add 0.63 GiB. At 32,768 tokens, they add 10.00 GiB. The metadata declares a maximum context of 131,072 tokens; this is not a guarantee of useful output quality at that length.
Reported parameters
70.554 billion
Cache architecture
llama
Metadata snapshot
2026-08-31
Weight, cache and planning totals
One sequence, F16 cache, full residency on one device. Context includes both prompt and generated tokens. All memory values are GiB (2³⁰ bytes).
| Precision / effective BPW | Context tokens | Weights | Cache & state | Planning total (+1 GiB budget) | Adjust |
|---|---|---|---|---|---|
| Q4_K_M scenario4.85 bits/weight | 2,048 | 39.84 | 0.63 | 41.46 | Calculator |
| Q4_K_M scenario4.85 bits/weight | 8,192 | 39.84 | 2.50 | 43.34 | Calculator |
| Q4_K_M scenario4.85 bits/weight | 32,768 | 39.84 | 10.00 | 50.84 | Calculator |
| Q8_0 scenario8.5 bits/weight | 2,048 | 69.82 | 0.63 | 71.44 | Calculator |
| Q8_0 scenario8.5 bits/weight | 8,192 | 69.82 | 2.50 | 73.32 | Calculator |
| Q8_0 scenario8.5 bits/weight | 32,768 | 69.82 | 10.00 | 80.82 | Calculator |
| 16-bit scenario16 bits/weight | 2,048 | 131.42 | 0.63 | 133.04 | Calculator |
| 16-bit scenario16 bits/weight | 8,192 | 131.42 | 2.50 | 134.92 | Calculator |
| 16-bit scenario16 bits/weight | 32,768 | 131.42 | 10.00 | 142.42 | Calculator |
Q4_K_M and Q8_0 here are assumed effective-BPW scenarios, not inspected GGUF files. Tensor mixtures and conversions can change actual weight storage. The calculator lets you inspect an actual artifact and choose your GPU before presenting a personalized result.
How these numbers are calculated
Weights = reported parameter count × effective bits per weight ÷ 8. Cache and state use the architecture-specific equations in the same engine as our calculator. Planning total = weights + cache/state + the stated runtime budget. For MoE, full residency includes all experts, not just active parameters per token.
Profile: llama.cpp / CUDA, Flash Attention enabled, micro-batch 512, prompt batch 2,048, no recurrent rollback snapshots. Engine 3.5.0; pinned runtime source. Multi-GPU, partial offload, training, speculative decoding and image/audio processing are outside these scenarios.
Model-specific assumptions and limitations
- Weights use parameter count × assumed effective BPW; mixed tensor types and conversion can change artifact size.
- Runtime allowance is an editable planning budget, not a measured compute-buffer prediction or an error bound.
- No model execution or hardware compatibility is certified. Available device memory may be lower than listed capacity.
Sources and reproducibility
Configuration at the pinned model revision · Model repository at that revision · Equations and external evidence
The parameter count and configuration come from our bundled metadata snapshot. This page is generated from that snapshot; it does not claim a live metadata check or a GPU benchmark.
Revision: 6f6073b423013f6a7d4d9f39144961bfbfbc386b. Open a table scenario to inspect that revision in the calculator.
Compare other models
- Qwen/Qwen3-Coder-Next — 79.674B parameters, qwen3_next
- microsoft/Phi-3.5-MoE-instruct — 41.873B parameters, phimoe
- mistralai/Mistral-Small-Instruct-2409 — 22.247B parameters, mistral