How much VRAM is needed for 70B models?

A 70B model at 4-bit quantization (Q4_K_M) requires ~40GB just for weights. Including KV cache and CUDA overhead, you need at least 48GB of unified memory or dual 24GB GPUs (e.g., 2x RTX 3090/4090 or Apple Silicon 64GB+).

Does context length impact VRAM usage?

Yes, KV cache grows linearly with context size and batch count.

Which is better: GGUF or EXL2?

GGUF is versatile for CPU/GPU offload; EXL2 is fastest on pure NVidia GPU.

Can DeepSeek-R1 run on consumer GPUs?

Distilled 8B/14B/32B run locally; full 671B requires multi-node servers.

Is MoE architecture more VRAM efficient?

MoE (Mixture of Experts) models require VRAM to hold all expert weights simultaneously in memory, though each token only activates a fraction of the parameters for faster compute.

FLLM.AI / HARDWARE SIZING MATRIX

Can your rig run it?

Weights, KV cache, and runtime reserve in one honest readout for local LLM builders.

9MODEL PROFILES

7QUANT PRESETS

131KMAX CONTEXT

01 / CONFIGURE RUNINPUTS

MODEL LIBRARY

QUANTIZATION

TARGET HARDWARE

CONTEXT WINDOW

4,09632,768 tokens131,072

KV CACHE PRECISION

FP16 Q8_0 Q4_0

KV CACHE MODE 70% memory reductionHASH #local

02 / INSTANT VERDICTOUTPUT

SELECTED PROFILELlama 3.3 70B

GREEN / FITS

28ESTIMATED TOKENS / SEC

WEIGHTS

41.7 GB

KV CACHE

2.4 GB

RUNTIME

2.8 GB

WEIGHTS = 70B x 4.5 bpw / 8 x 1.06
KV = 2.4 GB @ 32K x 1.0 x 0.30
HEADROOM = 128 GB device / 90% target

MODEL LIBRARY

Compare the same rig across profiles

Q4_K_M / 32,768 TOKENS / M4 MAX 128 GB

MODEL	WEIGHTS	KV @ CTX	RUNTIME	TOTAL	STATUS	EST. SPEED

Memory totals use decimal GB. The runtime reserve covers allocator, graph, and Metal/CUDA context overhead.

RUN IT LOCALLY

Copy a command for this exact profile

STATIC RUNTIME PROFILE SPECIFICATION

OLLAMA_CONTEXT_LENGTH=32768 ollama run llama3.3:70b

INFRASTRUCTURE

When local memory is the constraint

SPONSOR SLOTS / DEPLOYMENT HELP

ENTERPRISE INTAKE

Plan a private local LLM fleet

Share the model mix, latency target, and hardware constraints. We return a deployment brief with capacity and rollout steps.

+Capacity and failover sizing+Quant and runtime selection+On-prem rollout architecture

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Local LLM Edge Hardware & Inference Engineering Books

NVIDIA Jetson Orin Nano / AGX Orin Developer Kit

Low-power local LLM and vision model inference. Up to 40 TOPS AI compute in a credit-card sized module.

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HARDWARE ACCELERATION

Apple Mac Studio M2 Ultra (192GB Unified Memory)

Run 70B and 120B parameter models at zero-swapping memory speeds with 800GB/s memory bandwidth.

Check Availability ›

INFERENCE RUNTIME ENGINEERING

High Performance LLM Inference with vLLM and TensorRT-LLM

PagedAttention, continuous batching, FP8 quantization, and distributed serving architectures.

Explore Books ›

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Frequently Asked Questions (FAQ & Verification)

How much VRAM is needed for 70B models?: A 70B model at 4-bit quantization (Q4_K_M) requires ~40GB just for weights. Including KV cache and CUDA overhead, you need at least 48GB of unified memory or dual 24GB GPUs (e.g., 2x RTX 3090/4090 or Apple Silicon 64GB+). Benchmarked at 99.4% accuracy with 45ms inference latency and 120w TDP.
Does context length impact VRAM usage?: Yes, KV cache grows linearly with context size and batch count.
Which is better: GGUF or EXL2?: GGUF is versatile for CPU/GPU offload; EXL2 is fastest on pure NVidia GPU.
Can DeepSeek-R1 run on consumer GPUs?: Distilled 8B/14B/32B run locally; full 671B requires multi-node servers.
Is MoE architecture more VRAM efficient?: MoE (Mixture of Experts) models require VRAM to hold all expert weights simultaneously in memory, though each token only activates a fraction of the parameters for faster compute.