Llama 3.3 70B Instruct on Ryzen AI Max+ 395 128GB (128GB) — VRAM, KV Cache & Throughput

Llama 3.3 70B Instruct (70.55B total, 70.55B active, Dense GQA) uses 39.6 GiB of 4-bit weights (Q4_K_M), requiring 42.67 GiB total at 8K context and 50.17 GiB at 32K on Ryzen AI Max+ 395 128GB (~4.3 tok/s decode).

Interactive Sizing Workbench

Full Compatibility Report →

Open-Weight Model70.55B / 70.55B

Weights & QuantizationQ4_K_M · 39.6 GiB

Context Window8,192 (8K)

Target Hardware

128 GB · 256 GB/s

GPU Count (Tensor Parallel)

Concurrent Batch Size

KV Cache Precision

Inference Engine

System RAM Offload (CPU)0%

Fits with Safe Headroom

Measured GGUF shard size HF config.json ↗

Memory Allocation Breakdown (GiB) 42.67 GiB (45.82 GB) / 128 GiB (33%)

Model Weights: 39.6 GiB KV Cache & State: 2.5 GiB Runtime & Activation Buffers: 0.57 GiB System RAM Offload: 0 GiB

Required GPU Memory

42.67 GiB

45.82 GB (decimal)

Usable Hardware Memory

99.8 GiB

128 GB (physical)

Safe Headroom

+57.13 GiB

33% utilization

Est. Decode Throughput

~4.3 tok/s

70.55B active params

Est. Prefill Throughput

~176 tok/s

4K Prompt TTFT: ~23323 ms

Est. Cost / 1M Tokens

$21.32

Dense GQA · Llama 3.3

Recommended Hardware for Current Configuration

Best Single GPU

A40 48GB (48GB)

~$5,200

Best Value / Used GPU

RTX A6000 (48GB)

~$4,500

Apple Unified Memory

Ryzen AI Max+ 395 128GB

~$1,999

Cloud From

RunPod · $0.33 / hr

1x RTX A6000

Production Launch Commands

llama.cpp (GGUF)

llama-server -hf unsloth/Llama-3.3-70B-Instruct-GGUF:Q4_K_M \
  --ctx-size 8192 -ngl 999 --flash-attn

vLLM

vllm serve meta-llama/Llama-3.3-70B-Instruct \
  --max-model-len 8192 --gpu-memory-utilization 0.92

Live Token Speed Simulator (TTFT ~23323ms · ~4.3 tok/s)

[Llama 3.3 70B Instruct · 42.67 GiB] allocated on target GPU. Prefill completed in TTFT window; streaming autoregressive tokens at memory-bandwidth-bound rate...

Cloud GPU Comparison & Rent-vs-Buy TCO

Verified public rates across RunPod, Vast.ai, Vultr, and DigitalOcean.

Verified: 2026-11-04

Active usage (hours / day)4 h / day

Local hardware budget ($)

Electricity rate ($/kWh)

Purchase break-even

~154.1 months

Est. local power: $6.38 / mo

Provider	Recommended Instance	Total VRAM	Hourly Rate	Monthly at Selected Hours	Billing Mode	Deploy
RunPod	1x RTX A6000 (Community Cloud)	48 GB	$0.33 / hr	$40.13 / mo	Per-second	Deploy GPU ↗
Vast.ai	1x A100 80GB SXM (Verified Host · SXM4)	80 GB	$0.44 / hr	$53.75 / mo	Spot / On-demand	Deploy GPU ↗
*Vultr ($300 Free Credit)**	1x L40S 48GB (Dedicated Instance · $300 Credit*)	48 GB	$1.67 / hr	$203.19 / mo	Hourly	Deploy GPU ↗
Vultr Cloud GPU	1x L40S 48GB (Dedicated Instance)	48 GB	$1.67 / hr	$203.19 / mo	Hourly	Deploy GPU ↗
DigitalOcean	1x RTX 6000 Ada (Dedicated Instance)	48 GB	$1.57 / hr	$190.91 / mo	Per-second	Deploy GPU ↗

Rates reflect public listings on the verification date and fluctuate with regional inventory.

Fine-Tuning VRAM Requirements (QLoRA / LoRA / Full) — Llama 3.3 70B Instruct

QLoRA (4b) Fits with Safe Headroom

50.68 GiB

Model Weights: 39.6 GiB · Gradients + Optimizer: 9.88 GiB · Act: 1.2 GiB

Recommended GPU: RTX PRO 6000 Blackwell (96GB)

LoRA (16b) Exceeds Available GPU Memory

148.55 GiB

Model Weights: 131.41 GiB · Gradients + Optimizer: 15.52 GiB · Act: 1.62 GiB

Recommended GPU: Instinct MI300X 192GB (192GB)

Full BF16 Exceeds Available GPU Memory

541.63 GiB

Model Weights: 131.41 GiB · Gradients + Optimizer: 407.37 GiB · Act: 2.85 GiB

Recommended GPU: 4x B200 180GB (720GB)

Engineering Sizing Notes & Optimization Playbook

Quick Hardware Verdict

Ryzen AI Max+ 395 128GB (128GB physical, 99.8 GiB usable) runs 4-bit (Q4_K_M) Llama 3.3 70B Instruct at 8K context using 42.67 GiB with 57.13 GiB of safe headroom (~4.3 tok/s single-stream decode).

KV Cache & Runtime Tuning

Llama 3.3 70B Instruct uses Dense GQA (70.55B total, 70.55B active per token). Moving from 8K to 32K context expands FP16 KV cache from 2.5 GiB to 10 GiB; enabling 8-bit KV cache (llama.cpp -ctk q8_0 -ctv q8_0 or vLLM --kv-cache-dtype fp8) saves 5 GiB at 32K (reducing total VRAM to 45.17 GiB).

VRAM Scaling Across Context Windows (4K to 128K, 4-bit Weights)

Model weights remain constant while attention KV cache grows with context length and batch size.

Quantization Precision & Memory Matrix — Llama 3.3 70B Instruct @ 8K

Quantization Format	Weights (GiB)	KV Cache	Total VRAM (GiB)	Decode Speed	Compatibility (Ryzen AI Max+ 395 128GB)	Recommended GPU	Data Source
Native Weights (BF16 / FP8 / MXFP4 — Lossless) (BF16)	131.41 GiB	2.5 GiB	134.48 GiB (144.4 GB)	~1.2 tok/s	Exceeds Available GPU Memory	Instinct MI300X 192GB (192GB)	Official checkpoint size
FP8 / GGUF Q8_0 (8-bit — Near-Lossless) (Q8_0)	69.83 GiB	2.5 GiB	72.9 GiB (78.28 GB)	~2.5 tok/s	Fits with Safe Headroom	RTX PRO 6000 Blackwell (96GB)	Measured GGUF shard size
GGUF Q6_K (6-bit — High Precision) (Q6_K)	53.91 GiB	2.5 GiB	56.98 GiB (61.18 GB)	~3.2 tok/s	Fits with Safe Headroom	RTX PRO 6000 Blackwell (96GB)	Measured GGUF shard size
GGUF Q5_K_M (5-bit — Balanced) (Q5_K_M)	46.52 GiB	2.5 GiB	49.59 GiB (53.25 GB)	~3.7 tok/s	Fits with Safe Headroom	RTX PRO 6000 Blackwell (96GB)	Measured GGUF shard size
Q4_K_M / UD-Q4_K_XL / AWQ (4-bit — Recommended) (Q4_K_M)	39.6 GiB	2.5 GiB	42.67 GiB (45.82 GB)	~4.3 tok/s	Fits with Safe Headroom	A40 48GB (48GB)	Measured GGUF shard size
GGUF IQ4_XS (4.25-bit — Compact) (IQ4_XS)	35.33 GiB	2.5 GiB	38.4 GiB (41.23 GB)	~4.8 tok/s	Fits with Safe Headroom	A40 48GB (48GB)	Measured GGUF shard size
GGUF Q3_K_M (3-bit — Low VRAM) (Q3_K_M)	31.91 GiB	2.5 GiB	34.98 GiB (37.56 GB)	~5.3 tok/s	Fits with Safe Headroom	A40 48GB (48GB)	Measured GGUF shard size
GGUF UD-Q2_K_XL (2-bit — Extreme MoE) (UD-Q2_K_XL)	25.11 GiB	2.5 GiB	28.18 GiB (30.26 GB)	~6.7 tok/s	Fits with Safe Headroom	Radeon AI PRO R9700 32GB (32GB)	Measured GGUF shard size

Technical Specifications & FAQ

Does Llama 3.3 70B Instruct fit on Ryzen AI Max+ 395 128GB (128GB)?

In 4-bit (Q4_K_M) at 8,192 context, Llama 3.3 70B Instruct requires 42.67 GiB total GPU memory (39.6 GiB weights + 2.5 GiB KV cache + 0.57 GiB runtime buffers) against 99.8 GiB usable on Ryzen AI Max+ 395 128GB, delivering ~4.3 tok/s decode.

How much VRAM does Llama 3.3 70B Instruct need at 32K context?

At 32,768 context tokens, Llama 3.3 70B Instruct (Dense GQA) allocates 10 GiB for KV cache and state, bringing total 4-bit VRAM to 50.17 GiB (53.87 GB decimal).

Evaluate This Model on Other GPUs

RTX 3060 12GB (12GB)RTX 4060 Ti 16GB (16GB)RTX 4070 Ti Super (16GB)RTX 4080 Super (16GB)RTX 5070 12GB (12GB)RTX 5070 Ti (16GB)RTX 5080 (16GB)RTX 3090 (24GB)RTX 4090 (24GB)RTX 4090 D (24GB)RTX 5090 (32GB)RTX 5090 D (32GB)RTX A6000 (48GB)RTX 6000 Ada (48GB)RTX PRO 6000 Blackwell (96GB)DGX Spark (GB10 128GB) (128GB)L4 24GB (24GB)A40 48GB (48GB)L20 48GB (48GB)L40S 48GB (48GB)A800 80GB SXM (80GB)A100 80GB SXM (80GB)H800 80GB SXM (80GB)H100 80GB SXM (80GB)H20 96GB HBM3 (96GB)H20 141GB HBM3e (141GB)H200 141GB SXM (141GB)B200 180GB (180GB)B300 288GB Blackwell Ultra (288GB)Ascend 910B 64GB (昇腾) (64GB)Ascend 910C 128GB (昇腾) (128GB)Atlas 300I Duo 96GB (310P) (96GB)MLU590 80GB (寒武纪思元) (80GB)DCU K100-AI 64GB (海光深算) (64GB)MetaX C500 64GB (沐曦曦云) (64GB)Kunlunxin P800 64GB (昆仑芯) (64GB)MTT S4000 48GB (摩尔线程) (48GB)Radeon RX 7900 XTX 24GB (24GB)Radeon AI PRO R9700 32GB (32GB)Instinct MI300X 192GB (192GB)Instinct MI325X 256GB (256GB)Instinct MI355X 288GB (288GB)Arc Pro B60 24GB (24GB)Gaudi 3 128GB HBM2e (128GB)Mac mini M4 Pro 64GB (64GB)MacBook Pro M4 Max 128GB (128GB)Mac Studio M4 Ultra 256GB (256GB)Mac Studio M3 Ultra 512GB (512GB)

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