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UNIT · NVIDIA · GPU
48 GB VRAMworkstation·Reviewed May 2026

NVIDIA RTX 4090 48GB (China-mod)

NVDA · HARDWARE
NVIDIA RTX 4090 48GB (China-mod)

No editorial image yet — generic vendor mark shown. Credentials in spec table below.

Third-party physical modification of a stock GIGABYTE / ASUS / MSI RTX 4090. Chinese specialty shops (and GPVLab in the US) replace the 24 GB of GDDR6X with higher-density 32 Gb chips, doubling on-card VRAM to 48 GB. Same AD102 GPU core, same 384-bit memory bus, same 1008 GB/s bandwidth as a stock 4090 — only the memory density changes.

Released 2024·~$2400 street·1008 GB/s memory bandwidth
RUNLOCALAI SCORE
See full leaderboard →
534/ 1000
BB-tier
Estimated
Throughput
351/ 500
VRAM-fit
190/ 200
Ecosystem
200/ 200
Efficiency
22/ 100

Sub-scores sum to 763 / 1000. Headline = 763 × 0.70 (Estimated-confidence discount) = 534. This is an algorithmic performance-tier score — distinct from, and often lower than, the editorial “Our verdict” below, which weighs value and real-world fit (especially for hardware we haven’t measured yet). How scoring works →

Extrapolated from 1008 GB/s bandwidth — 121.0 tok/s estimated. No measured benchmarks yet.

WORKLOAD FIT
Try other hardware →

Plain-English: Runs 70B with care — snappy enough for a coding agent; vision models supported.

7B chat✓
Comfortable
14B chat✓
Comfortable
32B chat✓
Comfortable
70B chat~
Tight
Coding agent✓
Comfortable
Vision (≤8B VLM)✓
Comfortable
Long context (32K)✓
Comfortable
✓Comfortable — fits with headroom
~Tight — works, no slack
△Marginal — needs aggressive quant
✗Doesn't fit usefully

Verdicts extrapolated from catalog VRAM + bandwidth + ecosystem flags. Hover any chip for the rationale. Want measured numbers? Submit your own run with runlocalai-bench --submit.

BLK · OVERVIEW

Overview

Third-party physical modification of a stock GIGABYTE / ASUS / MSI RTX 4090. Chinese specialty shops (and GPVLab in the US) replace the 24 GB of GDDR6X with higher-density 32 Gb chips, doubling on-card VRAM to 48 GB. Same AD102 GPU core, same 384-bit memory bus, same 1008 GB/s bandwidth as a stock 4090 — only the memory density changes. The 48 GB capacity opens up workloads that the stock 4090 can't host: Llama 3.3 70B Q4 with comfortable context, dual-32B models in parallel, longer context windows on 32B-class models. **GamersNexus has covered the modification process** (youtube.com/watch?v=TcRGBeOENLg). Community sellers list on Taobao and Bilibili; GPVLab takes US/EU orders. Important caveats: not a vendor-blessed product. No NVIDIA warranty. Power draw and thermals match a stock 4090; operators commonly power-limit to 350 W to reduce noise and heat. Real-world benchmark data is community-reported and not yet in our editorial benchmark queue.

Retailers we'd check:Amazon

Search-fallback link — editorial hasn't yet curated a retailer URL for this card. Approx. $2400.

Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.

BLK · SPECS

Specs

VRAM48 GB
Power draw (peak)450 W
Released2024
Backends
CUDA
Vulkan

Models that fit

Open-weight models small enough to run on NVIDIA RTX 4090 48GB (China-mod) with usable context.

all-MiniLM-L6-v2
0.022B · other
FLUX.1 [dev]
12B · other
Llama 3.1 8B Instruct
8B · llama
Kokoro 82M
0.082B · other
Nomic Embed Text v1.5
0.137B · other
BGE Large EN v1.5
0.335B · other
Qwen 3 0.6B
0.6B · qwen
Qwen3.6 27B
27B · qwen

Frequently asked

What models can NVIDIA RTX 4090 48GB (China-mod) run?

With 48GB VRAM, the NVIDIA RTX 4090 48GB (China-mod) runs 70B models in 4-bit quantization, plus everything smaller. See the model list below for tested combinations.

Does NVIDIA RTX 4090 48GB (China-mod) support CUDA?

Yes — NVIDIA RTX 4090 48GB (China-mod) is an NVIDIA card with full CUDA support, the most mature local-AI backend. llama.cpp, Ollama, vLLM, and ExLlamaV2 all run natively.

How much does NVIDIA RTX 4090 48GB (China-mod) cost?

Current street price for NVIDIA RTX 4090 48GB (China-mod) is around $2400. Prices vary by region and supply.

Where next?

Buyer guides
  • Best GPU for local AI →
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  • Best used GPU for local AI →
Troubleshooting
  • CUDA out of memory →
  • Ollama running slowly →
  • ROCm not detected →
  • Model keeps crashing →

Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.

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