NVIDIA RTX 5050

NVIDIA RTX 5050: Released July 1, 2025 at a $249 MSRP, the RTX 5050 is the smallest desktop Blackwell die this site tracks: a cut-down GB207-300 chip with 20 SMs, 2,560 CUDA cores, 80 5th-gen Tensor Cores, and 20 RT cores at a 130W TDP (NVIDIA's own spec page and Wikipedia's GeForce RTX 50-series table agree on every figure here). It's the only desktop 50-series card that still ships with GDDR6 instead of GDDR7: 8GB on a 128-bit bus at a 20 Gbps pin rate for 320 GB/s, versus the 28 Gbps GDDR7 every other desktop Blackwell card in this lineup uses. That's a cost decision, not a limitation of the die itself: NVIDIA's laptop GeForce RTX 5050, built on the same 2,560-core silicon, ships with 8GB of GDDR7 instead (VideoCardz's launch coverage). The result: this card ties the RTX 5060 for the smallest VRAM capacity in the desktop stack (8GB), but doesn't tie its bandwidth: 320 GB/s is 28.6% below the RTX 5060's 448 GB/s despite both sharing the exact same 128-bit bus, purely because one runs GDDR6 and the other GDDR7.

This card's roughly 7.6 GB of usable VRAM (this site reserves 5% of the spec-sheet 8GB) covers the small end of the ladder with real headroom, not just a bare fit: this site's calculator puts Llama 3.2 3B at 58.3 tok/s at its recommended Q6_K (3.99 GB), Qwen 2.5 3B at 73.1 tok/s at Q6_K (3.19 GB), and Gemma 3 4B at 54.9 tok/s at Q6_K (4.24 GB). The 7-8B class is the real boundary, and it's closer than a blanket "possible at aggressive quant" suggests: Llama 3.1 8B's recommended Q5_K_M (7.58 GB) and Qwen 2.5 7B's recommended Q6_K (7.51 GB) both clear the ~7.6 GB ceiling (Llama 3.1 8B by only about 20 MB), decoding at 30.7 and 31.0 tok/s. Qwen3 8B's recommended Q5_K_M (7.73 GB) tips about 130 MB over that same line into CPU offload, dropping to 30.1 tok/s despite needing barely more memory than the models that do fit natively. Overall, 27 of this site's 99 tracked models fit natively in this card's VRAM at 8k context, exactly as many as the otherwise-identical-capacity RTX 5060, because VRAM capacity, not bandwidth, decides the fits/offload line; the RTX 5060's extra bandwidth just makes those same 26 models faster (Llama 3.2 3B: 58.3 tok/s here vs 81.6 tok/s there, a 40% gap tracking the 448-vs-320 GB/s bandwidth ratio almost exactly). Independent llama.cpp/Ollama benchmarks specific to this card are sparse (it's a budget gaming SKU first, not one reviewers typically bench for LLM workloads), so treat the tok/s figures above as this site's own bandwidth-modeled estimates, not community-verified numbers.

Full CUDA support and the same sm_120 Blackwell compute capability as every other desktop 50-series card, so it inherits the identical early-driver rough edges: NVIDIA's own engineering blog measured a ~27% LM Studio/llama.cpp speedup on the sibling RTX 5080 just from upgrading to the CUDA 12.8 runtime Blackwell requires, and a very old llama.cpp, Ollama, or PyTorch build may still not recognize this GPU's architecture. The 5th-gen Tensor Cores support NVFP4 in hardware, but at 8GB there's little capacity left to exploit it; the format buys speed on models that already fit, not headroom for larger ones. At $249, this card undercuts the previous-generation RTX 4060's $299 MSRP by trading specs rather than simply beating them: The FPS Review's review (8/10) found the RTX 4060 packs 20% more CUDA cores (3,072 vs 2,560) while this card offers 18% more memory bandwidth (320 vs 272 GB/s), at a 130W TDP versus the 4060's 115W. Since LLM decode is bandwidth-bound rather than compute-bound, that trade favors this card's tok/s over its Ada-generation predecessor at the identical 8GB ceiling; the same review wished for 12GB of VRAM "to really be a game changer" at this price, and that 8GB ceiling is the real ecosystem gotcha for local LLM work: Ollama, LM Studio, and llama.cpp all install and run without issue, but the practical ceiling is a 7B-class model, not a compute or driver limitation.