Ada's budget option. Same 8 GB / 128-bit envelope as the 3050 with better per-watt efficiency — the models that fit are the same, the watts are fewer.
Spec note: the RTX 4050 is not listed on NVIDIA’s official comparison page (knowledge/rtx-40-series.md, fetched 2026-09-24). The values above are the standard published board specs, kept as supplementary.
Ada's efficiency is real — 135 W for roughly 3050-level token speed — but the memory path is the 3050's path: 128-bit GDDR6 at 224 GB/s. Since generation re-reads the weights for every token, this card generates at the same ~30–40 t/s on 8B that a 3050 does. Ada's win here is everything the benchmark doesn't show: lower idle draw, cooler case, cheaper electricity on a box that runs all day.
8 GB buys the 8B class with room: Q8_0 at 16K, Q4–Q6 with bigger context. 13B Q4_K_M squeezes in at short context (~16–23 t/s); 13B Q3 keeps it comfortable at the cost of quality. Beyond that the wall is absolute — 27B needs 19 GB before the KV cache even exists.
2560 cores keep prompt processing in the ~300 t/s neighborhood on small models: fine for chat, noticeably slower than the mid-range when you feed it a long document.
The MTP question: if your model ships a built-in multi-token-prediction head
(Qwen 3.5/3.6/3.8, Gemma 4), the one-flag speculative pass
(--spec-type draft-mtp --spec-draft-n-max 2 --parallel 1) is worth
+40–70% (estimated) on token generation here; the community record has no 4050 A/B yet; low-bandwidth cards tend to gain the most at n-max 2, so the estimate leans on that end of the record. The price is ~0.6–2 GB of VRAM, the
output is bit-identical to the unaccelerated run, and models without MTP heads (Llama class) see
nothing. Full tuning rules in the MTP guide.
| Model | Size on GPU | Token gen | Token gen (MTP est.) | Prompt proc. | Fits? |
|---|---|---|---|---|---|
| 8B (Llama 3.1 8B, Mistral) | ~4.9 GB | ~30–40 t/s | ~42–68 t/s | ~300–400 t/s | ✅ Q8_0, 16K+ ctx |
| 13B Q4_K_M | ~8.5 GB | ~16–23 t/s | ~23–39 t/s | ~250–350 t/s | ⚠️ tight, short ctx only |
| 13B Q3_K_M | ~6.7 GB | ~18–26 t/s | ~26–44 t/s | ~300 t/s | ✅ with quality loss |
| 27B | ~19 GB | — | — | — | ❌ no |
MTP est. = rough prior with --spec-type draft-mtp for models that ship MTP heads (Qwen 3.5/3.6/3.8, Gemma 4); Llama-class models get no MTP speedup. Actual gains depend on model, quant, context length, llama.cpp build and your card — sweep --spec-draft-n-max, don't trust the column blindly. MTP guide →
Figures assume full offload (-ngl 99), ~8K context and batch 2048. Token speed tracks the memory bus almost linearly, so treat ranges as class estimates — your llama.cpp build and the model's attention layout shift them.
| Quant | GGUF size | 1× 4050 (8 GB) | 2× 4050 (16 GB) |
|---|---|---|---|
| Q4_K_M | ~19 GB | ❌ ~11 GB over | ❌ ~3 GB over |
| Q5_K_M | ~22 GB | ❌ | ❌ |
| Q6_K | ~25 GB | ❌ | ❌ |
| Q8_0 | ~33 GB | ❌ | ❌ |
Nothing 27B-class fits at any quant, alone or paired. Four cards (32 GB) gets Q4_K_M to roughly ~7–9 t/s at 4–8K context; partial offload on one or two cards is a few t/s — proof of concept, not a setup to live in.
No NVLink anywhere in the Ada lineup: splits ride PCIe 4.0 at a ~25–35% tax, and 8 GB tiles decide what fits.
| Build | GPU power | VRAM | 13B Q4 | 27B Q4_K_M |
|---|---|---|---|---|
| 2× 4050 | 270 W | 16 GB | ~22–30 t/s | ❌ ~3 GB over |
| 4× 4050 | 540 W | 32 GB | ~35–45 t/s | ~7–9 t/s, 4–8K ctx |
Four slots of 4050 is the only way to 27B at all, and the pace says what the build is for. A workstation board with four working slots is the hardware cost of that idea.
When a model won't fit, -ngl N parks the tail layers on the CPU. The mental model
that saves pain: offloaded layers don't run at a fraction of GPU speed — they run at CPU
speed. Handy moves: peel the last 2–4 layers off to reclaim ~0.5–1 GB of VRAM for almost
nothing; keep MoE experts on CPU with --n-cpu-moe; park the output embedding with
-ot output=CPU on big models. Budget 32 GB system RAM for 27B-class offload, 64 GB for
70B. And never offload the KV cache — the session becomes unusable.
llama-server -m 13b-q4_k_m.gguf -ngl 99 -sm layer -c 8192 --tensor-split 1,1
llama-server -m llama-3.1-8b-q6_k.gguf -ngl 99 -c 8192 -b 2048 -ub 2048 --host 127.0.0.1 --port 8080
The 4050 is the 3050 with Ada's power curve: identical model ceiling, better thermals, cheaper to leave on. It earns its place as a small-model box; it does not earn it as an LLM investment. One step up (4060 Ti 16G) doubles the VRAM and the conversation.