AMD · RDNA 3
Best AI Models for AMD Radeon RX 7900 XT (20 GB)
Runs 27–31B models well; 35B MoE only just fits.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 27.6 t/s | 27.6 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 27.6 t/s | 27.6 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 85.3 t/s | 85.3 tok/s | Q4_K_M · 16.5 GB | |
| 4 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 98.1 t/s | 98.1 tok/s | Q4_K_M · 13.1 GB | |
| 5 | LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 89.5 t/s | 89.5 tok/s | Q4_K_M · 15.0 GB |
| # | Model | Speed | Coding Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 27.6 t/s | 27.6 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 85.3 t/s | 85.3 tok/s | Q4_K_M · 16.5 GB | |
| 3 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 27.6 t/s | 27.6 tok/s | Q4_K_M · 17.9 GB | |
| 4 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 98.1 t/s | 98.1 tok/s | Q4_K_M · 13.1 GB | |
| 5 | LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 89.5 t/s | 89.5 tok/s | Q4_K_M · 15.0 GB |
| # | Model | Speed | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 27.6 t/s | 27.6 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.5 27BAlibaba Q4_K_M · 17.9 GB · 27.6 t/s | 27.6 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 85.3 t/s | 85.3 tok/s | Q4_K_M · 16.5 GB | |
| 4 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 98.1 t/s | 98.1 tok/s | Q4_K_M · 13.1 GB | |
| 5 | LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 89.5 t/s | 89.5 tok/s | Q4_K_M · 15.0 GB |
| # | Model | Speed | Math Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 27.6 t/s | 27.6 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 27.6 t/s | 27.6 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 85.3 t/s | 85.3 tok/s | overall | Q4_K_M · 16.5 GB |
| 4 | LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 89.5 t/s | 89.5 tok/s | Q4_K_M · 15.0 GB | |
| 5 | Q4_K_M · 6.5 GB · 77.3 t/s | 77.3 tok/s | Q4_K_M · 6.5 GB |
| # | Model | Speed | Long context Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 75.5 t/s | 75.5 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 147 t/s | 147 tok/s | Q4_K_M · 3.4 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 98.1 t/s | 98.1 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Mistral Nemo Instruct 2407Mistral AI Q4_K_M · 11.0 GB · 59.5 t/s | 59.5 tok/s | Q4_K_M · 11.0 GB | |
| 5 | LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 89.5 t/s | 89.5 tok/s | Q4_K_M · 15.0 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 147 t/s | 147 tok/s | Q4_K_M · 3.4 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 98.1 t/s | 98.1 tok/s | Q4_K_M · 13.1 GB | |
| 3 | LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 89.5 t/s | 89.5 tok/s | Q4_K_M · 15.0 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 85.3 t/s | 85.3 tok/s | Q4_K_M · 16.5 GB | |
| 5 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 75.5 t/s | 75.5 tok/s | Q4_K_M · 6.6 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 27.6 t/s | 27.6 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 27.6 t/s | 27.6 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 85.3 t/s | 85.3 tok/s | Q4_K_M · 16.5 GB | |
| 4 | Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 59.7 t/s | 59.7 tok/s | Q4_K_M · 10.9 GB | |
| 5 | Q4_K_M · 6.5 GB · 77.3 t/s | 77.3 tok/s | Q4_K_M · 6.5 GB |
The RX 7900 XT is a solid mid-range card in the 16–20 GB class: it runs 27–31B models well. It is more efficient per watt than most 16–20 GB cards, and at its $899 MSRP it gave the best speed per dollar of the cards compared. RDNA 3 generation, released 2022.
RX 7900 XT strengths and weaknesses
Strengths
- Runs 35B MoE models: Qwen3.6 35B A3B at 74.5 tok/s in 19.8 GB
Weaknesses
- 70B models don't fit: Llama 3.3 70B Instruct needs 51.3 GB at Q4 vs 20 GB here
- Slow prompt reading: A 4K-token prompt on Qwen3.8 27B waits 10.0 s for the first token
- ROCm, not CUDA: No FP8 weights; MXFP4 only through llama.cpp, not vLLM
Speed per dollar and per watt
On Qwen3.5 9B (9B params) at Q4_K_M. New prices are compared with new, used with used. The last two columns are the speed you get per $100 of price and per 100 W of board power; higher is better. The RX 7900 XT has no current price, so it is shown at its MSRP.
| Card | New price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|
RX 7900 XT20 GB | about $899 | 75.5 | 8.4 | 24.0 |
RTX 507012 GB | about $840 | 68.7 | 8.2 | 27.5 |
RX 7900 XTX24 GB | about $1,130 | 90.6 | 8.0 | 25.5 |
RTX 5070 Ti16 GBBest new value | about $1,170 | 91.6 | 7.8 | 30.5 |
RX 907016 GB | about $800 | 60.4 | 7.6 | 27.5 |
RX 9070 XT16 GB | about $815 | 60.4 | 7.4 | 19.9 |
RTX 508016 GB | about $1,690 | 98.1 | 5.8 | 27.3 |
RTX 509032 GB | about $5,376 | 183 | 3.4 | 31.8 |
| Card | Used price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|
RTX 3060 12GB12 GBBest used value | about $329 | 36.8 | 11.2 | 21.6 |
RX 7900 XTX24 GB | about $1,000 | 90.6 | 9.1 | 25.5 |
RTX 5070 Ti16 GB | about $1,100 | 91.6 | 8.3 | 30.5 |
RTX 4070 Ti SUPER16 GB | about $900 | 68.7 | 7.6 | 24.1 |
RTX 408016 GB | about $1,100 | 73.3 | 6.7 | 22.9 |
RTX 4080 SUPER16 GB | about $1,155 | 75.2 | 6.5 | 23.5 |
RTX 309024 GB | about $1,500 | 95.7 | 6.4 | 27.3 |
RTX 509032 GB | about $6,400 | 183 | 2.9 | 31.8 |
Cost per token: the RX 7900 XT against 12–32 GB cards
On Qwen3.5 9B (9B params, Q4_K_M, 4K context). Speed, cost per token and power per token depend on the model.
- Card, MSRP
- $0.76
- Power
- $0.17
Renting costs as much as buying after ≈4,700 hours (≈3.2 years at 4 h a day).
Each dot is a card; hover or tap it for its numbers, click to open it. Cards without a price for the chosen basis are left out. How we calculate cost per token
Cards people weigh against the RX 7900 XT, each with the one reason it is worth a look. Speeds on Qwen3.5 9B.
Output speed depends on memory bandwidth (800 GB/s). Grey rows need more than 20 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB75.5tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB50.5tok/s
- 27–31BQwen3.8 27B · Q4_K_M · 17.9 GB27.6tok/s
- 74.5tok/s
- 70BLlama 3.3 70B Instruct · not at 4-bitneeds 51.3 GB
- 120B MoEGPT OSS 120B · not at 4-bitneeds 70.8 GB
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
All 1117 models on the RX 7900 XT
Featured onlyBest quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
| Q4_K_M | 1.2 GB | 448.6 t/s | 128K | S100, Runs great | ||
Openai GPTChat | Q4_K_M | 0.1 GB | 6000.0 t/s | 1K | S100, Runs great | |
| Q4_K_M | 5.7 GB | 97.8 t/s | 8K | S91, Runs great | ||
Phi 1 5ChatCode | Q4_K_M | 0.9 GB | 510.6 t/s | 2K | S100, Runs great | |
DialoGPT SmallChat | Q4_K_M | 0.1 GB | 4000.0 t/s | 1K | S100, Runs great | |
Sarvam 30B BF16Chat | Q4_K_M | 19.9 GB | 104.7 t/s | 131K | C44, Usable | |
STEM Oracle 27BChatMath | Q3_K_M | 18.1 GB | 33.1 t/s | — | B58, Decent | |
| Q4_K_M | 0.3 GB | 1600.0 t/s | 2K | S100, Runs great | ||
Xglm 564MChat | Q4_K_M | 0.4 GB | 1297.3 t/s | 2K | S100, Runs great | |
Llama 160MChat | Q4_K_M | 0.5 GB | 1021.3 t/s | 2K | S100, Runs great | |
Qwen2 VL 2B InstructVision | Q4_K_M | 2.1 GB | 285.7 t/s | 33K | S100, Runs great | |
NuExtractChat | Q4_K_M | 4.2 GB | 141.2 t/s | 4K | S96, Runs great | |
| Q4_K_M | 6.2 GB | 89.1 t/s | 8K | S89, Runs great | ||
| Q4_K_M | 10.4 GB | 62.9 t/s | 33K | A84, Runs well | ||
Codegen 350M MonoChatCode | FP16 | 0.8 GB | 623.4 t/s | 2K | S100, Runs great |
Specs
- Architecture
- RDNA 3
- VRAM
- 20 GB GDDR6
- Memory bandwidth
- 800.0 GB/s
- FP16 (dense)
- 103.20 TFLOPS
- Stream processors
- 5,376
- TDP
- 315 W
- Released
- 2022-12-13
- MSRP
- $899
RX 7900 XT FAQ
Is the RX 7900 XT still worth it in 2026?
At its MSRP of $899 you get 8.4 tok/s per $100 on Qwen3.5 9B, which ranks 1st of the 8 cards compared here; it is also the 3rd fastest out of 18 cards with 16–20 GB. It runs 27–31B models well; 35B MoE only just fits.
What is the largest model the RX 7900 XT can run?
Qwen3.6 35B A3B (36.0B) is the largest at 4-bit or better: at IQ4_XS it uses 19.8 GB of 20 GB, and runs at about 74.5 tok/s.
Can the RX 7900 XT run Qwen3.8 27B?
Yes. Qwen3.8 27B fits at Q4_K_M, using 17.9 GB of 20 GB, and it is the best model that runs well on this card.
Can the RX 7900 XT run 70B models?
No. Llama 3.3 70B Instruct needs 51.3 GB at Q4, more than the 20 GB on this card.
How many tokens per second does the RX 7900 XT give?
About 27.6 tok/s on Qwen3.8 27B at Q4_K_M, with the first token after 10.0 s on a 4K-token prompt.
What power supply does the RX 7900 XT need?
Plan a 650 W PSU (2× the 315 W TDP).
Is 20 GB enough for coding and agents?
Yes for coding: Qwen3.8 27B, the best of the 6 coding models that fit, runs at about 28 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.
Is renting cheaper than buying an RX 7900 XT?
An RTX 3090 (24 GB) rents from $0.19/hr. Renting costs as much as buying after ≈4,700 hours (≈3.2 years at 4 h a day).