HARDWARE FIT CHECK • Qwen 3.8 27B Instruct + AMD Radeon RX 7900 XT (20GB GDDR6)21.93 GB / 20 GB VRAM

Can I Run Qwen 3.8 27B Instruct on AMD Radeon RX 7900 XT (20GB GDDR6)?

Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Qwen 3.8 27B Instruct (27.2B) on AMD Radeon RX 7900 XT (20GB GDDR6) (20 GB).

⚡ PARTIAL OFFLOAD21.93 GB (110%)~6 t/s

Model & Target Hardware

interactive
Agentic Coding, Terminal Workflows & Multi-File Refactoring
GPU Topology / Quantity:Single GPU / Unified SoC
Qwen 3.8 27B Instruct on AMD Radeon RX 7900 XT (20GB GDDR6)

⚠️ Partial Offload — Slow CPU/RAM Spillover

⚡ PARTIAL OFFLOAD
Required VRAM
21.93 GB
110% of hardware capacity (20 GB)
Est. Decode Speed
~6 tok/s
Dense model @ 800 GB/s
Available Headroom
-1.93 GB
Needs +1.93 GB more VRAM
Memory Allocation & Headroom
21.93 GB allocated of 20 GB capacity
110% utilized
Weights: 16.32 GB
KV: (32K): 4.29 GB
Overhead: 1.32 GB
Deficit: -1.93 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~6 tok/s) is memory-bandwidth bound at batch size = 1 on AMD Radeon RX 7900 XT (20GB GDDR6) (800 GB/s). Prompt prefill / TTFT (~87 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run qwen3.8:27b

Local Hardware Configurations & Pricing

⚡ Partial Offload: Dedicated 100% GPU Options
High-bandwidth VRAM specs and current market prices
Consumer GPU✓ Fits Model
32 GB VRAM

NVIDIA GeForce RTX 5090 (32GB GDDR7)

Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization

1792 GB/s575W TDP
Consumer GPU✓ Fits Model
24 GB VRAM

NVIDIA GeForce RTX 4090 (24GB GDDR6X)

The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models

1008 GB/s450W TDP
Multi-GPU Rig✓ Fits Model
48 GB VRAM

2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)

Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8

936 GB/s700W TDP
Consumer GPU
20 GB VRAM

AMD Radeon RX 7900 XT (20GB GDDR6)

Sub-$700 GPU with 20GB VRAM fitting 14B Q8 and 27B Q4 models

800 GB/s315W TDP

Quantization Precision Matrix

32K ctx • KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw54.4 GB4.29 GB61.34 GB (307%)0 t/sOOM
Q8_08.5 bpw28.83 GB4.29 GB34.88 GB (174%)0 t/sOOM
Q6_K6.56 bpw22.3 GB4.29 GB28.12 GB (141%)0 t/sOOM
Q5_K_M5.68 bpw19.31 GB4.29 GB25.03 GB (125%)~5 t/sPARTIAL
Q4_K_M4.8 bpw16.32 GB4.29 GB21.93 GB (110%)~6 t/sPARTIAL
FP44.25 bpw14.42 GB4.29 GB19.96 GB (100%)~30 t/sFITS
IQ3_XS3.3 bpw11.15 GB4.29 GB16.58 GB (83%)~38 t/sFITS

Export Full Architecture & Infrastructure Spec

Get an executive-ready PDF report with exact memory math, hardware benchmarks, and cost comparisons.

FAQ & Reference

Frequently Asked Questions: Qwen 3.8 27B Instruct on AMD Radeon RX 7900 XT (20GB GDDR6)

How much VRAM does Qwen 3.8 27B Instruct need on AMD Radeon RX 7900 XT (20GB GDDR6)?

At standard 4-bit quantization (Q4_K_M) with 32K context, Qwen 3.8 27B Instruct requires 21.93 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will Qwen 3.8 27B Instruct achieve on AMD Radeon RX 7900 XT (20GB GDDR6)?

Based on AMD Radeon RX 7900 XT (20GB GDDR6)'s 800 GB/s memory bandwidth, expected decode speed is approximately 6 tokens per second.

Can I expand context beyond 32K tokens?

Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.