RTX 4000 SFF Ada vs RTX PRO 4000 SFF Blackwell for Local LLMs: 20GB vs 24GB at 70W

Introduction

A 70W low-profile workstation card is a niche purchase, and the people who need one usually have no other option. OEM small-form-factor towers such as the Dell Precision compact line and Lenovo ThinkStation P3 Tiny and Ultra ship with power supplies that have no PCIe 8-pin connector, and with half-height slots. Quiet homelab boxes and 2U short-depth servers have the same constraints. In those machines, a consumer RTX card isn't a choice at all. The choice is between NVIDIA's two slot-powered SFF cards: the RTX 4000 SFF Ada Generation, launched in 2023, and its successor, the RTX PRO 4000 SFF Blackwell.

Both cards draw a maximum of 70W, both carry ECC memory, and both are dual-slot, low-profile boards. The differences that matter for local LLM inference are memory capacity (20GB vs 24GB), memory bandwidth (280 GB/s vs 432 GB/s) and the PCIe interface (Gen4 x16 vs Gen5 x8).

Step 0: is your workload memory-bound or compute-bound?

Answer this before you compare anything else. Token generation, the part of inference where the model writes its answer one token at a time, reads every active weight once per token. Its speed is limited mainly by memory bandwidth, so more GB/s means more tokens per second. Prompt processing (prefill), where the model reads your input, is limited by compute. Prefill matters most for retrieval-augmented generation (RAG), long documents and agent loops that re-send large contexts.

Capacity comes before either: a model that doesn't fit in VRAM falls back to system RAM and slows down by an order of magnitude. So the decision order is: does it fit (capacity), how fast does it talk (bandwidth), how fast does it read (compute).

Key Takeaways

• The PRO 4000 SFF Blackwell has 24GB vs 20GB and 432 GB/s vs 280 GB/s in the same 70W envelope, per PNY's and Hardware Corner's published specs.
• Published runs on the RTX 4000 SFF Ada put Llama 3.1 8B Q4_K_M at 44.4 tok/s and Qwen2.5 14B Q4_K_M at 24.7 tok/s, per LocalScore.
• On the 20GB Ada card, 32B models need a sub-Q4 quant (IQ4_XS ran at about 16.4-16.9 tok/s, per Hardware Corner). The 24GB card fits Q4_K_M with room for context.
• No public LLM benchmarks for the PRO 4000 SFF Blackwell were found as of November 2026. The bandwidth ratio suggests generation up to about 1.5x faster, but that is an estimate, not a measurement.
• If your chassis takes a full-height, 170W card, an RTX 3060 12GB matches the Ada SFF on small models for a fraction of the price.

Spec-delta table

Sources: VRAM, power and PCIe for the Ada card come from NVIDIA's RTX 4000 SFF page, and its 6,144 CUDA cores from NVIDIA's RTX 4000 SFF Ada datasheet; its 280 GB/s bandwidth comes from Hardware Corner, and its 160-bit bus from the entry in llama.cpp discussion #15013. All Blackwell figures come from PNY's spec page. Check current pricing on the live buttons; listing prices for both cards move often, and the Ada card is now widely sold used.

One detail is easy to miss: the Blackwell card's PCIe 5.0 x8 link matches Gen4 x16 bandwidth only in a Gen5 slot. In an older Gen4 workstation it runs at Gen4 x8, half the host bandwidth. For single-card inference with the whole model in VRAM, that barely matters after the model loads. It matters more if you offload layers to system RAM.

What tok/s does the RTX 4000 Ada class deliver on 7-9B models?

Read the card column carefully. The XiongjieDai and llama.cpp #15013 rows were measured on the full-height RTX 4000 Ada, a higher-power card with the same 20GB memory configuration. It is not the 70W SFF. The SFF-specific LocalScore run is lower: 44.4 tok/s against 58.59 tok/s on the full-height card's closest comparable row. Different runtimes and settings explain part of that gap, and the power cap explains the rest. Hardware Corner's SFF guide reports 58.59 tok/s for Llama 3.1 8B Q4_K_M, which matches the full-height XiongjieDai figure exactly, so treat it with caution. Plan on mid-40s tok/s for an 8B Q4 model on the SFF card. That is still faster than you can read.

What does the PRO 4000 SFF Blackwell measure?

No published llama.cpp, Ollama, vLLM or LocalScore run for the RTX PRO 4000 SFF Blackwell could be found as of November 2026. The card is new, it sells mostly into OEM workstation channels, and benchmark sites haven't caught up. So the numbers below are an estimate, labelled as one.

Token generation tracks memory bandwidth closely, so the simplest projection scales the Ada SFF's measured results by the bandwidth ratio: 432 / 280 = 1.54x.

Treat these as ceilings. Real cards rarely reach their full theoretical bandwidth during inference, and the 70W cap limits clocks on both generations. If you find a published run for this card, trust it over this table. Measured rows will be added to the RTX 4000 SFF Ada benchmark page and its Blackwell counterpart as public data appears.

What fits in 20GB vs 24GB?

The table below estimates weight sizes from each GGUF quant's average bits per weight (about 4.85 for Q4_K_M, 5.7 for Q5_K_M, 6.56 for Q6_K, 8.5 for Q8_0, 16 for FP16), multiplied by the parameter count. It is arithmetic, not a measurement. Allow roughly 1GB more for the CUDA runtime and compute buffers.

KV cache comes on top. For a model with Qwen2.5-32B's attention layout (64 layers, 8 KV heads, head dimension 128), FP16 KV cache costs about 0.25MB per token: roughly 2GB at 8K context and 8GB at 32K. Qwen2.5 14B's layout (48 layers) costs about 0.19MB per token: 1.5GB at 8K and 6GB at 32K. gpt-oss 20B uses a sliding window on half its layers, so its KV cache stays under about 1GB even at 32K by the same arithmetic.

The practical upshot:

• 14B models: both cards run Q8_0 with 8K context. The 24GB card adds room for 32K context at Q6_K.
• 27B models: the 20GB card runs Q4_K_M with a short context. The 24GB card runs Q5_K_M or Q4_K_M with 16K+ context.
• 32B models: the 20GB card needs IQ4_XS or Q3_K_M, which is what Hardware Corner ran. The 24GB card runs Q4_K_M with about 12K tokens of FP16 KV cache (19.9GB of weights plus about 1GB of overhead leaves about 3GB), or about 24K with 8-bit KV cache.

For a per-model breakdown across more GPUs, see our best 24GB GPUs for local LLMs and best 16GB GPUs for local LLMs.

Prefill vs generation: where GDDR7 helps and where 70W limits it

GDDR7's bandwidth lifts generation, the memory-bound phase, close to linearly, as long as the GPU core can keep up. At 70W both cards spend most of their power budget on the memory and core during generation. The Blackwell card's 8,960 CUDA cores and fifth-generation tensor cores, per PNY, give it more compute per watt for prefill. LocalScore measured the Ada SFF at 2,004 tok/s of prefill on Llama 3.1 8B Q4_K_M and 1,051 tok/s on Qwen2.5 14B Q4_K_M. A 10,000-token RAG prompt on 14B therefore takes about 10 seconds before the first output token on the Ada SFF.

Blackwell also adds native FP4 tensor-core support. Runtimes that use NVFP4 or MXFP4 weights, including gpt-oss's native MXFP4 checkpoints in recent llama.cpp and vLLM builds, can use it. Ada falls back to slower paths for those formats. For agent workloads with long, repeated contexts, that is the Blackwell card's largest practical advantage after capacity.

What the 70W cap costs: under sustained load, both cards hold lower clocks than their full-height siblings. That is why the SFF Ada's LocalScore result sits below the full-height card's XiongjieDai result. Expect the same pattern from Blackwell versus the full-height RTX PRO 4000 Blackwell, a separate, higher-power card listed as the PNY RTX PRO 4000 Blackwell.

Context-length impact: what the extra 4GB buys at 27B Q4

Take a 27B model at Q4_K_M (about 16.4GB of weights) and add about 1GB of runtime overhead:

• 20GB card: about 2.6GB left for KV cache. With a 0.25MB-per-token layout, that is roughly 10K tokens at FP16 KV, or about 20K with 8-bit KV cache.
• 24GB card: about 6.6GB left, roughly 26K tokens at FP16 KV, or about 52K with 8-bit KV.

The extra 4GB buys about 16K tokens of FP16 context at this size. For document Q&A, coding agents or long chat histories, that is a bigger day-to-day difference than the speed gain.

Perf-per-watt and perf-per-dollar

Using board power ratings, not measured draw:

On the 7-9B runs collected on our RTX 3060 12GB benchmark page, the consumer MSI RTX 3060 12GB matches or beats the Ada SFF at small-model generation. On 12-15B models its median is about 29.4 tok/s across 27 published runs from 12 sources, slightly ahead of the SFF Ada's 24.7 tok/s on Qwen2.5 14B. It costs a fraction of either workstation card. It also needs a full-height, dual-slot space, a PCIe 8-pin connector and a 170W budget, and it has 12GB with no ECC. In a full-size tower, it is the value pick. In a Dell Precision compact or a ThinkStation Tiny, it doesn't fit.

On price: at the time of writing, the tracked new listing for the Blackwell SFF sits at roughly 1.8x the tracked listing for the Ada SFF, and used Ada cards sell for less still. For 7-14B models, the Ada card delivers more tokens per dollar. The Blackwell card earns its premium only when you need the 24GB or the FP4 path.

Can you run two in an SFF chassis?

Physically, rarely. Most compact OEM towers have one x16 slot with room for a dual-slot low-profile card, and a second slot that is x4 electrically or blocked by the first card. Some 2U servers and mini-ITX homelab builds with bifurcation risers can take two. The main limit is airflow: two 70W blower-style cards back to back in a cramped chassis will throttle without directed airflow.

Multi-GPU scaling for inference works. llama.cpp splits layers across cards, and vLLM supports tensor parallelism. XiongjieDai's benchmarks ran Llama 3 70B Q4_K_M across four full-height RTX 4000 Ada cards at 7.33 tok/s. Two Blackwell SFF cards would give 48GB, enough for a 70B model at Q4 with a short context, at 140W total. For that budget, also look at a single high-VRAM card like the RTX A6000 if your chassis allows it.

Verdict matrix

Get the RTX 4000 SFF Ada if…

• Your main models are 7-14B (Llama 3.1 8B, Qwen 14B, Mistral, Phi)
• You can buy used at a good price
• You want a proven card with years of public benchmark data

Get the RTX PRO 4000 SFF Blackwell if…

• You run 27B-32B models at Q4_K_M, or need 16K+ context at 27B
• You use FP4/MXFP4 models like gpt-oss and want native tensor-core support
• You need a new card with full warranty in a 24/7 workstation

Get a consumer RTX 3060 12GB instead if…

• Your chassis takes a full-height card and has a PCIe 8-pin connector
• Your models are 8B-14B and you don't need ECC
• Price matters more than power draw or size

Recommended pick

For most people building a slot-powered AI box in late 2026, the RTX PRO 4000 SFF Blackwell is the pick when the budget allows. The 24GB capacity opens up 27B-32B models at proper quantization, and the 54% bandwidth increase should show up in generation speed once public runs arrive. If your models top out at 14B, buy a used RTX 4000 SFF Ada and spend the difference on system RAM or storage.

Bottom line

Capacity decides this comparison, then bandwidth. Both cards fit the same slots and draw the same 70W. The Blackwell card's extra 4GB moves a 32B model from "only with a compromised quant" to "runs at Q4_K_M", and its GDDR7 should speed up everything else. For 7-14B work, the Ada card is still a capable, cheaper choice.

Live price comparison

Compare both cards side by side, with live prices, on the RTX 4000 SFF Ada vs RTX PRO 4000 SFF Blackwell comparison page. The PRO 4000 SFF Blackwell often sells through workstation resellers and eBay rather than Amazon, so check both buttons.

Related guides

• RTX A6000 48GB vs RTX 3090 for production inference
• Best 24GB GPUs for local LLMs
• Best 16GB GPUs for local LLMs

Citations and sources

1. NVIDIA — RTX 4000 SFF Ada Generation (accessed 2026-11-09)
2. PNY — NVIDIA RTX PRO 4000 SFF Blackwell specifications (accessed 2026-11-09)
3. LocalScore — RTX 4000 SFF Ada accelerator results (accessed 2026-11-09)
4. XiongjieDai — GPU-Benchmarks-on-LLM-Inference (accessed 2026-11-09)
5. Hardware Corner — RTX 4000 SFF Ada for LLMs (accessed 2026-11-09)
6. llama.cpp discussion #15013 — CUDA performance results (accessed 2026-11-09)

This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.