Qwen3-8B VRAM Requirements, GGUF Sizes & GPU Guide

Interactive Qwen3-8B Memory & Context Simulator

Weight Quantization (bpw)

KV Cache Precision

Target GPU / Unified Memory Tier

Devices (TP)

Context Length (tokens) 32,768 tokens

Memory Breakdown & Bandwidth Ceiling

Tight Fit (>90% Usable)

Model Weights: 44.5% KV: 43.2% Overhead: 12.3%

Total Required Memory

10.41 GB

Per-Device Allocation: 10.41 GB

Model Weights

4.63 GB

KV Cache (FP16): 4.50 GB

Est. Single-Stream Decode

23.0 tok/s

Runtime & TP Overhead: 1.28 GB

Max Context Before OOM

42,971

tokens

Architecture KV compression saves 75.0% vs naive MHA FP16 (18.00 GB).

Precision & Quantization VRAM Matrix (8K / 32K / 128K Context)

Total GPU memory required across weight precisions and context lengths (batch = 1, FP16 KV cache)
Precision / Format	Effective bpw	Weights Only	Total @ 8K ctx	Total @ 32K ctx	Total @ 128K ctx	Recommended Tier (@ 32K)
FP16 / BF16	16.00 bpw	15.27 GB	18.00 GB	21.37 GB	34.87 GB	RTX 3090 24GB
FP8	8.50 bpw	8.11 GB	10.62 GB	14.00 GB	27.50 GB	RTX 4060 Ti 16GB
Q8_0	8.50 bpw	8.11 GB	10.62 GB	14.00 GB	27.50 GB	RTX 4060 Ti 16GB
Q6_K	6.56 bpw	6.26 GB	8.72 GB	12.09 GB	25.59 GB	RTX 4060 Ti 16GB
Q5_K_M	5.69 bpw	5.43 GB	7.86 GB	11.23 GB	24.73 GB	RTX 3060 12GB
Q4_K_M	4.85 bpw	4.63 GB	7.03 GB	10.41 GB	23.91 GB	RTX 3060 12GB
AWQ INT4	4.50 bpw	4.30 GB	6.69 GB	10.06 GB	23.56 GB	RTX 3060 12GB
IQ4_XS	4.25 bpw	4.06 GB	6.44 GB	9.82 GB	23.32 GB	RTX 3060 12GB

Hardware Compatibility & Decode Speed (Qwen3-8B @ Q4_K_M, 32K Context)

Fit status, memory utilization, and estimated decode throughput across flagship GPUs and Apple Silicon
Hardware Tier	Memory & Bandwidth	Required (32K)	Fit Verdict	Est. Decode Speed
RTX 3090 24GB	24 GB · 936 GB/s	10.41 GB (43%)	Fits Comfortably	59.7 tok/s
RTX 4090 24GB	24 GB · 1008 GB/s	10.41 GB (43%)	Fits Comfortably	64.3 tok/s
RTX 5090 32GB	32 GB · 1792 GB/s	10.41 GB (33%)	Fits Comfortably	114.4 tok/s
Apple M-Max 64GB	64 GB · 546 GB/s	10.00 GB (16%)	Fits Comfortably	32.8 tok/s
Mac Studio 128GB	128 GB · 546 GB/s	10.00 GB (8%)	Fits Comfortably	32.8 tok/s
Mac Studio 192GB	192 GB · 800 GB/s	10.00 GB (5%)	Fits Comfortably	48.1 tok/s
NVIDIA A100 80GB	80 GB · 2039 GB/s	10.41 GB (13%)	Fits Comfortably	137.8 tok/s
NVIDIA H100 80GB	80 GB · 3350 GB/s	10.41 GB (13%)	Fits Comfortably	226.4 tok/s

Verified Transformer Architecture Parameters

Checkpoint ID: Qwen/Qwen3-8B
Ollama Tag: qwen3:8b
License / Release: Apache-2.0 · 2025-Q2
Topology & Attention: Dense Transformer
Attention Mechanism: Grouped-Query Attention (GQA)
Total Params (B): 8.2B
Active Params / Token (B): 8.2B
Hidden Layers / Hidden Size: 36 / 4096
Query Heads / KV Heads: 32Q / 8KV (d=128)
Vocab / Max Position: 151,936 / 40,960

Quantized formats store per-block scale factors and zero-points alongside packed integer weights. A 4-bit GGUF Q4_K_M checkpoint averages 4.85 effective bits per parameter (not 4.00), while Q8_0 averages 8.50 bpw. For Mixture-of-Experts models, all routed and shared experts remain resident in memory to prevent token-by-token host paging.

Standard Grouped-Query Attention allocates 2 × L × H_kv × d_head × S × B × bytes_kv across layers. DeepSeek Multi-head Latent Attention (MLA) compresses keys and values into L × (d_kv_lora + d_qk_rope) × S × B × bytes_kv, slashing long-context memory by over 90%. Hybrid sliding-window models cap local-attention layers at min(S, window_size) while scaling global layers linearly with S.

Quick-Start vLLM & Ollama Commands for Qwen3-8B

vllm serve Qwen/Qwen3-8B \
  --tensor-parallel-size 1 \
  --max-model-len 32768 \
  --max-num-seqs 8 \
  --gpu-memory-utilization 0.95 \
  --kv-cache-dtype fp8 \
  --enable-prefix-caching \
  --port 8000

# Save as Modelfile, then run: ollama create qwen3-8b-custom -f Modelfile
FROM qwen3:8b
PARAMETER num_ctx 32768
PARAMETER num_batch 512
PARAMETER flash_attn true

# Environment variables for ollama serve:
# OLLAMA_NUM_PARALLEL=8 OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama serve