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Curated Real-World Hardware Dilemmas & Benchmarking Case Studies

GPU buyers often face difficult choices between newer architectures with less VRAM versus older cards with larger frame buffers. Below are verified engineering findings across the four most contested matchups:

RTX 3060 12G vs RTX 4060 8G

Winner: RTX 3060 12G
Benchmark Model: DeepSeek-R1-Distill-Qwen-14B

The classic $300 dilemma. The older RTX 3060 12G fits the entire 14B model in VRAM, delivering ~24 tok/s. The newer RTX 4060 8G runs out of memory, spills layers to DDR4 RAM over PCIe, and crawls at ~1.8 tok/s.

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RTX 3090 24G vs RTX 4070 Ti Super 16G

Winner: RTX 3090 24G
Benchmark Model: Qwen 2.5 Coder 32B (Q4_K_M)

Used flagship vs modern upper-midrange. The RTX 3090 with 24GB VRAM loads the complete 32B coding model with zero offload. The 4070 Ti Super with 16GB suffers memory deficits, throttling heavily on complex coding tasks.

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RTX 5090 32G vs RTX 4090 24G

Winner: RTX 5090 32G
Benchmark Model: DeepSeek-R1-Distill-Llama-70B

Blackwell GDDR7 (1,792 GB/s) vs Ada GDDR6X (1,008 GB/s). The RTX 5090's 32GB frame buffer allows running 70B models at IQ4_XS with 40+ tok/s, while the 4090 requires layer offload or heavy quantizations.

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RTX 4090 24G vs Apple M3 Max 36G

Winner: Context-dependent
Benchmark Model: DeepSeek-R1-Distill-Qwen-32B

The RTX 4090 delivers unmatched raw generation speed (60+ tok/s) thanks to 1,008 GB/s bandwidth. The M3 Max offers a larger 36GB memory pool at lower power consumption, handling larger context windows quietly.

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The Three Iron Laws of Local AI Hardware Selection

1. VRAM Capacity is Binary (Pass/Fail)If a model exceeds your VRAM pool, layers must spill across the PCIe bus to host DDR RAM. Because PCIe 4.0 x16 operates at only 31.5 GB/s compared to 1,000+ GB/s VRAM, speed collapses by 95%.
2. Memory Bandwidth Dictates TPSFor batch=1 autoregressive token generation, extra CUDA cores or TFLOPS will not increase tokens/sec. Generation speed is strictly: TPS = Bandwidth (GB/s) / Model Size (GB) × Efficiency.
3. KV Cache Scales Linearly (Unless MLA)Standard GQA models consume 4GB to 12GB of VRAM for long context windows. Models with DeepSeek MLA compress KV memory by over 80%, allowing 64k context on cards that would otherwise crash.

Frequently Asked Questions on GPU Head-to-Head Comparisons

Engineering insights on memory bandwidth, bus latencies, and architectural trade-offs:

Why does the RTX 3060 12GB outperform the RTX 4060 8GB in local AI?
Local AI inference requires the entire model weights and KV cache to fit in VRAM. For 14B models like DeepSeek-R1 14B, total VRAM demand is ~11.9 GB. The RTX 3060's 12GB frame buffer holds the model completely, running at ~28 tok/s. The RTX 4060 has only 8GB, forcing 4GB of layers to offload across the PCIe bus to DDR4/DDR5 system RAM, slowing inference down by 90% to ~1.8 tok/s.
Should I buy a used RTX 3090 24GB or a new RTX 4070 Ti Super 16GB for AI?
For AI workloads, the RTX 3090 24GB is decisively superior. The extra 8GB of VRAM allows running 32B models (like Qwen 2.5 Coder 32B and DeepSeek R1 32B) and 70B models at IQ3 quantizations with zero offloading. The RTX 4070 Ti Super is limited to 16GB, restricting it to 14B models or short-context 32B models.
How much faster is the RTX 5090 GDDR7 compared to the RTX 4090 in AI inference?
The RTX 5090 features GDDR7 memory delivering 1,792 GB/s bandwidth compared to 1,008 GB/s on the RTX 4090 (a 78% increase). In batch=1 autoregressive token generation, throughput scales directly with bandwidth, increasing generation speeds from ~42 tok/s on the 4090 to ~65+ tok/s on the 5090 for 32B models.
Does memory bandwidth or CUDA core count matter more for LLM tokens per second?
Memory bandwidth matters far more. In single-user (batch=1) autoregressive decoding, every single token requires reading all model weights from VRAM once. Since compute utilization during decoding is very low (typically under 20%), the memory bus is the 100% bottleneck. CUDA cores and TFLOPS primarily accelerate the prompt prefill phase (TTFT), not token generation speed.