HARDWARE FIT CHECK • DeepSeek-R1 Distill Qwen 32B + AMD Radeon RX 7900 XT (20GB GDDR6)25.41 GB / 20 GB VRAM

Can I Run DeepSeek-R1 Distill Qwen 32B 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 DeepSeek-R1 Distill Qwen 32B (32.8B) on AMD Radeon RX 7900 XT (20GB GDDR6) (20 GB).

⚡ PARTIAL OFFLOAD25.41 GB (127%)~5 t/s

Model & Target Hardware

interactive
Local Step-by-Step Math, Logic & Code Verification
GPU Topology / Quantity:Single GPU / Unified SoC
DeepSeek-R1 Distill Qwen 32B on AMD Radeon RX 7900 XT (20GB GDDR6)

⚠️ Partial Offload — Slow CPU/RAM Spillover

⚡ PARTIAL OFFLOAD
Required VRAM
25.41 GB
127% of hardware capacity (20 GB)
Est. Decode Speed
~5 tok/s
Dense model @ 800 GB/s
Available Headroom
-5.41 GB
Needs +5.41 GB more VRAM
Memory Allocation & Headroom
25.41 GB allocated of 20 GB capacity
127% utilized
Weights: 19.68 GB
KV: (32K): 4.29 GB
Overhead: 1.44 GB
Deficit: -5.41 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~5 tok/s) is memory-bandwidth bound at batch size = 1 on AMD Radeon RX 7900 XT (20GB GDDR6) (800 GB/s). Prompt prefill / TTFT (~73 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 deepseek-r1:32b

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↑ Upgrade
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 bpw65.6 GB4.29 GB72.94 GB (365%)0 t/sOOM
Q8_08.5 bpw34.77 GB4.29 GB41.03 GB (205%)0 t/sOOM
Q6_K6.56 bpw26.9 GB4.29 GB32.88 GB (164%)0 t/sOOM
Q5_K_M5.68 bpw23.29 GB4.29 GB29.15 GB (146%)0 t/sOOM
Q4_K_M4.8 bpw19.68 GB4.29 GB25.41 GB (127%)~5 t/sPARTIAL
FP44.25 bpw17.38 GB4.29 GB23.03 GB (115%)~6 t/sPARTIAL
IQ3_XS3.3 bpw13.45 GB4.29 GB18.96 GB (95%)~32 t/sFITS

Export Full Architecture & Infrastructure Spec

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FAQ & Reference

Frequently Asked Questions: DeepSeek-R1 Distill Qwen 32B on AMD Radeon RX 7900 XT (20GB GDDR6)

How much VRAM does DeepSeek-R1 Distill Qwen 32B need on AMD Radeon RX 7900 XT (20GB GDDR6)?

At standard 4-bit quantization (Q4_K_M) with 32K context, DeepSeek-R1 Distill Qwen 32B requires 25.41 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will DeepSeek-R1 Distill Qwen 32B 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 5 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.