Llama 3.1 70B VRAM requirements: 48GB, 64GB, 80GB or 96GB?

Short answer: Meta labels Llama 3.1 70B as a 70-billion-parameter model with a 128K context window. Raw weight arithmetic is about 35GB at 4-bit, 70GB at 8-bit and 140GB at 16-bit before file-format overhead, KV cache, runtime buffers or other GPU use. In practice, a compact 4-bit build usually points to a 48GB-or-larger plan, while long context can move the requirement sharply upward. Always inspect the exact artifact and runtime rather than treating “70B” as a complete memory specification.

Llama 3.1 70B weight-memory floor

The transparent lower-bound formula is parameters × bits per parameter ÷ 8. This uses Meta’s marketed 70B size; it is planning arithmetic, not a measured allocation or an exact download-size claim.

Nominal precision	Raw decimal weight floor	Approximate binary equivalent	What it means
4-bit	35GB	32.6GiB	A compact artifact can target 48GB, but exact file size and workload headroom decide fit
5-bit	43.75GB	40.7GiB	Often a tight 48GB plan before cache and runtime allocations
6-bit	52.5GB	48.9GiB	Exceeds a 48GB label before overhead; points toward 64GB+
8-bit	70GB	65.2GiB	Exceeds 64GB and can be tight even on 80GB after complete-workload allocations
16-bit	140GB	130.4GiB	Requires a much larger unified-memory or multi-device plan

Illustrative KV-cache payload at 8K, 32K and 128K

Meta’s official architecture listing gives Llama 3.1 70B an 8,192-element model dimension, 80 layers, 64 attention heads and eight KV heads. That implies a 128-element head dimension. For one sequence with FP16 key and value entries:

2 × 80 layers × 8 KV heads × 128 head dimension × 2 bytes × token slots

Allocated token slots	Illustrative FP16 K+V payload	Combined with 4-bit raw floor
8,192	2.5GiB	About 35.1GiB before runtime overhead
32,768	10GiB	About 42.6GiB before runtime overhead
131,072	40GiB	About 72.6GiB before runtime overhead

Can Llama 3.1 70B run on 24GB?

Not as a complete nominal 4-bit weight set inside 24GB: the raw 4-bit floor is already about 35GB. CPU offload or split placement can make a larger artifact load, but performance and memory traffic become workload- and interconnect-dependent. A very low-bit community conversion is a different artifact with its own quality and compatibility tradeoffs.

Is 48GB enough?

Forty-eight gigabytes is the first common one-device tier that can plausibly hold a compact 4-bit Llama 3.1 70B artifact plus a modest cache and runtime state. It is not an unconditional fit: a 5-bit artifact, long context, large batch, desktop display use or multiple simultaneous sequences can consume the remaining margin. Check the exact file size and measure the longest representative request.

What do 64GB, 80GB and 96GB change?

Memory tier	Practical planning role	Main boundary
64GB	More room for 4-bit/5-bit artifacts, longer context or runtime overhead	The raw 8-bit floor is already about 70GB
80GB	Can target 8-bit or very long-context 4-bit workloads	Eight-bit weights plus cache and buffers can still be tight
96GB	Meaningful extra room for 8-bit or long-context 4-bit plans	Still far below the 16-bit raw-weight floor
128GB+	Large unified-memory or multi-device configurations	Shared memory is not dedicated VRAM; multi-device memory is not automatically pooled

Why 128K context changes the hardware question

Meta publishes 128K as the model family’s context length, but capacity grows with allocated tokens and concurrent sequences. At the full 131,072 slots, the illustrative FP16 cache payload is about 40GiB for one sequence. Add that to a nominal 4-bit weight floor and the combined payload is already about 72.6GiB before runtime allocations. If the runtime uses a smaller or quantized cache, the result can be lower; if it preallocates, batches or serves concurrent users, it can be higher.

Single GPU, unified memory, offload or multiple GPUs?

• One 48GB GPU: simplest compact-4-bit target, with context and overhead measured carefully.
• 64GB–96GB accelerator: more capacity, but software support, passive cooling, power connectors and exact product variant can matter as much as the label.
• Large unified-memory system: can expose one large address space, but CPU/GPU sharing and bandwidth differ from dedicated VRAM.
• CPU offload: expands what can load; it does not preserve all-GPU speed.
• Multiple GPUs: requires an explicit tensor/layer placement strategy. Two 24GB cards are not automatically one transparent 48GB pool.

A reproducible validation checklist

1. Record the exact Llama 3.1 70B artifact, revision and quantization.
2. Record the runtime build, backend and supported kernels.
3. Set context, cache precision, batch size, concurrency and maximum output.
4. Add display use, buffers and other device allocations.
5. Measure peak device and host memory during the longest representative request.
6. Test output quality for the selected quantization.
7. Record prompt processing, generation speed and time to first token separately.

Sources and limits

• Meta Llama 3.1 70B Instruct model card
• Meta Llama model architecture registry
• Meta Llama 3.1 model card source
• HighVRAM methodology

Reviewed October 25, 2026. Meta publishes the 70B label, 128K context and GQA architecture. Weight and cache numbers above are transparent planning arithmetic, not measured peak-memory claims. HighVRAM has not independently benchmarked every configuration. Capacity does not establish speed, output quality or value. Meta publishes Llama 3.3 70B as the newer release; compare its memory plan with Llama 3.1. This page remains scoped to Llama 3.1 70B.