AMD · RDNA 4 · 16 GB VRAM tier
Best AI Models for AMD Radeon RX 9070 (16 GB)
Runs 14B models well; 27–31B only just fits.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 60.4 t/s | 60.4 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 118 t/s | 118 tok/s | Q4_K_M · 3.4 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 86.6 t/s | 86.6 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Phi 4Microsoft Q4_K_M · 12.5 GB · 40.3 t/s | 40.3 tok/s | Q4_K_M · 12.5 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 46.7 t/s | 46.7 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed | Coding Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | GPT OSS 20BOpenAIHow to run → Q4_K_M · 13.1 GB · 86.6 t/s | 86.6 tok/s | Q4_K_M · 13.1 GB | |
| 2 | Q4_K_M · 7.9 GB · 72.5 t/s | 72.5 tok/s | Q4_K_M · 7.9 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 60.4 t/s | 60.4 tok/s | overall | Q4_K_M · 6.6 GB |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 46.7 t/s | 46.7 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Q4_K_M · 6.5 GB · 61.8 t/s | 61.8 tok/s | Q4_K_M · 6.5 GB |
| # | Model | Speed | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 60.4 t/s | 60.4 tok/s | Q4_K_M · 6.6 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 86.6 t/s | 86.6 tok/s | Q4_K_M · 13.1 GB | |
| 3 | Qwen3 8BAlibaba Q4_K_M · 7.6 GB · 69.6 t/s | 69.6 tok/s | Q4_K_M · 7.6 GB | |
| 4 | Q4_K_M · 7.9 GB · 72.5 t/s | 72.5 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 46.7 t/s | 46.7 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed | Long context Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 60.4 t/s | 60.4 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 118 t/s | 118 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Mistral Nemo Instruct 2407Mistral AI Q4_K_M · 11.0 GB · 47.6 t/s | 47.6 tok/s | Q4_K_M · 11.0 GB | |
| 4 | Q4_K_M · 7.9 GB · 72.5 t/s | 72.5 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 46.7 t/s | 46.7 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 118 t/s | 118 tok/s | Q4_K_M · 3.4 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 86.6 t/s | 86.6 tok/s | Q4_K_M · 13.1 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 60.4 t/s | 60.4 tok/s | Q4_K_M · 6.6 GB | |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 46.7 t/s | 46.7 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Phi 4Microsoft Q4_K_M · 12.5 GB · 40.3 t/s | 40.3 tok/s | Q4_K_M · 12.5 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 60.4 t/s | 60.4 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 118 t/s | 118 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 47.8 t/s | 47.8 tok/s | Q4_K_M · 10.9 GB | |
| 4 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 135 t/s | 135 tok/s | Q4_K_M · 5.0 GB | |
| 5 | Q4_K_M · 6.5 GB · 61.8 t/s | 61.8 tok/s | Q4_K_M · 6.5 GB |
The RX 9070 is a solid mid-range card in the 16 GB class: it runs 14B models well. It is among the most efficient per watt in its tier, and fair value at ≈$800. RDNA 4 generation, released 2025.
RX 9070 strengths and weaknesses
Strengths
- Runs 35B MoE models: Qwen3.6 35B A3B at 74.7 tok/s in 15.7 GB
- Reads long prompts fast: A 4K-token prompt on Qwen3.5 9B starts answering in 2.5 s
Weaknesses
- 70B models don't fit: Llama 3.3 70B Instruct needs 51.3 GB at Q4 vs 16 GB here
- ROCm, not CUDA: No FP8 weights; MXFP4 only through llama.cpp, not vLLM
- Priced well above MSRP: ≈$800 new vs its $549 MSRP (+46%)
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.
| Card | New price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|
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 |
RX 9060 XT 16GB16 GB | about $590 | 30.2 | 5.1 | 18.9 |
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 |
RTX 507012 GB | about $736 | 68.7 | 9.3 | 27.5 |
RX 907016 GB | about $650 | 60.4 | 9.3 | 27.5 |
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 |
RX 9070 XT16 GB | about $790 | 60.4 | 7.6 | 19.9 |
RTX 4070 Ti SUPER16 GB | about $900 | 68.7 | 7.6 | 24.1 |
RX 9060 XT 16GB16 GB | about $480 | 30.2 | 6.3 | 18.9 |
Cost per token: the RX 9070 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, new price
- $0.84
- Power
- $0.15
Renting costs as much as buying after ≈4,000 hours (≈2.8 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 9070, each with the one reason it is worth a look. Speeds on Qwen3.5 9B.
Output speed depends on memory bandwidth (640 GB/s). Grey rows need more than 16 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB60.4tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB40.4tok/s
- 25.6tok/s
- 74.7tok/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 1057 models on the RX 9070
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 |
|---|---|---|---|---|---|---|
Cosmos Reason2 8BVisionChatReasoningFunctions | Q4_K_M | 8.4 GB | 66.3 t/s | — | S85, Runs great | |
Tiny Lfm2ForCausalLM 2.5ChatFunctions | Q4_K_M | 0.5 GB | 1280.0 t/s | 128K | S100, Runs great | |
Qwen3.5 0.8BVisionFunctions | Q4_K_M | 0.9 GB | 441.4 t/s | 262K | S100, Runs great | |
Tiny NemotronHForCausalLM 3.5 LightningChatFunctions | Q4_K_M | 0.3 GB | 38400.0 t/s | 262K | S100, Runs great | |
Tiny Lfm2ForCausalLMChatFunctions | Q4_K_M | 0.5 GB | 1280.0 t/s | 128K | S100, Runs great | |
Molmo2 4BVision | Q4_K_M | 4.7 GB | 112.9 t/s | 37K | S94, Runs great | |
Qwen3 4B Mamba3 Hybrid Stage2b Kd Bias0ChatFunctions | Q4_K_M | 4.7 GB | 115.3 t/s | 41K | S94, Runs great | |
Tmax 9BChatFunctions | Q4_K_M | 6.2 GB | 64.6 t/s | 262K | A84, Runs well | |
Wald 4BChatReasoningFunctions | Q4_K_M | 3.4 GB | 117.8 t/s | 262K | S94, Runs great | |
CAJAL 4BChat | Q4_K_M | 4.8 GB | 145.5 t/s | — | S97, Runs great | |
BitCPM CANN 8BChat | Q4_K_M | 5.6 GB | 74.3 t/s | 33K | S86, Runs great | |
| Q4_K_M | 0.3 GB | 1280.0 t/s | 4K | S100, Runs great | ||
DiffuRefill 1BChat | BF16 | 2.6 GB | 147.7 t/s | 2K | S97, Runs great | |
| Q4_K_M | 7.1 GB | 83.1 t/s | — | S88, Runs great | ||
K2 Horizon 7BChatFunctions | Q4_K_M | 8.1 GB | 64.0 t/s | 524K | A84, Runs well |
Specs
- Architecture
- RDNA 4
- VRAM
- 16 GB GDDR6
- Memory bandwidth
- 640.0 GB/s
- FP16 (dense)
- 145.00 TFLOPS
- Stream processors
- 3,584
- TDP
- 220 W
- Released
- 2025-03-06
- MSRP
- $549
RX 9070 FAQ
Is the RX 9070 still worth it in 2026?
At ≈$800 you get 7.6 tok/s per $100 on Qwen3.5 9B, which ranks 4th of the 8 cards compared here; it is also the 6th fastest out of 17 cards with 16 GB. It runs 14B models well; 27–31B only just fits.
What is the largest model the RX 9070 can run?
Gemma 4 26B A4B IT (25.8B) is the largest at 4-bit or better: at Q4_K_S it uses 15.5 GB of 16 GB, and runs at about 78.6 tok/s. At 3-bit, a bigger model fits too: Gemma 2 27B IT (27.2B) at IQ3_M, 15.0 GB.
Can the RX 9070 run Qwen3.5 9B?
Yes. Qwen3.5 9B fits at Q4_K_M, using 6.6 GB of 16 GB, and it is the best model that runs well on this card.
Can the RX 9070 run 70B models?
No. Llama 3.3 70B Instruct needs 51.3 GB at Q4, more than the 16 GB on this card.
How many tokens per second does the RX 9070 give?
About 60.4 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 2.5 s on a 4K-token prompt.
What power supply does the RX 9070 need?
Plan a 450 W PSU (2× the 220 W TDP).
Is 16 GB enough for coding and agents?
Yes for coding: GPT OSS 20B, the best of the 2 coding models that fit, runs at about 87 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.
Is renting cheaper than buying an RX 9070?
An RTX 3090 (24 GB) rents from $0.20/hr. Renting costs as much as buying after ≈4,000 hours (≈2.8 years at 4 h a day).