Slot width decides more server builds than VRAM does. A 2U chassis with one riser, or a workstation with four x16 slots one bracket apart, rejects the dual-slot cards on every “best GPU” list. So the best single-slot GPU for you is the fastest one that fits and that your chassis can cool. Only a handful of current cards qualify, and they differ more than their matching shrouds suggest.
We rented the three blower cards on vast.ai, ran the same llama.cpp benchmark and NVENC transcode on each, and checked the two low-profile cards against their datasheets. You get tokens per second, peak power, AV1 results and chassis limits for local LLMs, Plex or Jellyfin and dense servers. Every new benchmark below was run on rented RTX PRO 4000 Blackwell, RTX A4000 and RTX 4000 Ada cards in October 2026.
Start with the short list
If you already know your chassis, here are the picks. The RTX PRO 4000 Blackwell won every test we ran, but it costs more than double the A4000. For most people the A4000 is still the card to buy:
- Best overall for AI: NVIDIA RTX PRO 4000 Blackwell 24GB
- Best value: NVIDIA RTX A4000 16GB
- Best for AV1 transcoding on a budget: NVIDIA RTX 4000 Ada 20GB
- Best for 1U and 2U servers: NVIDIA L4 24GB
- Best with no power cable: NVIDIA RTX 2000E Ada 16GB (integrator channel only)
Every card on that list is one bracket wide according to its maker. Several “compact” cards did not make it. The RTX PRO 4000 Blackwell SFF Edition, the RTX 2000 Ada, the RTX PRO 2000 Blackwell and Intel’s Arc Pro B50 are all dual-slot low-profile designs. They fit a short case but take two brackets, so they are the wrong answer when slot count is the limit.
See how we tested the cards
Each blower card ran in its own vast.ai container from the nvidia/cuda:12.8.1-devel-ubuntu24.04 image. We compiled llama.cpp from one commit (7aca0d2) on all three, so the GPU was the only variable. Both models are unsloth Qwen3 builds at Q4_K_M: the 8B (4.68 GiB) fits anything, and the 14B (8.38 GiB) is about the largest a 16 GB card holds with room for context. We sampled nvidia-smi once a second through each run and kept the peak power.
What we did not test matters as much. We skipped renting the L4, and the RTX 2000E Ada had no rental offers, so both are judged on datasheets and live listings. Rented hosts also differ. The RTX 4000 Ada host capped the card at 115 W of its 130 W limit on a PCIe Gen 3 link, and the RTX PRO 4000 host linked at Gen 4 rather than Gen 5. Neither changes token generation much, because generation is bound by memory bandwidth inside the card. Prompt processing on the Ada would likely be a little higher at full power.
You can repeat the benchmark on your own card with the same flags. Build llama.cpp with CUDA first, then point llama-bench at a model with every layer offloaded:
./build/bin/llama-bench -m Qwen3-8B-Q4_K_M.gguf -ngl 99 -p 512 -n 128 -r 3On the RTX PRO 4000 Blackwell the 8B run printed this. The tg128 row is generation speed, the number you feel while a chat answer streams:
| model | size | params | backend | ngl | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: |
| qwen3 8B Q4_K - Medium | 4.68 GiB | 8.19 B | CUDA | 99 | pp512 | 5242.17 ± 125.71 |
| qwen3 8B Q4_K - Medium | 4.68 GiB | 8.19 B | CUDA | 99 | tg128 | 108.24 ± 0.30 |
build: 7aca0d2 (1)The ± figures are the spread across three repetitions. Generation varied by less than half a percent on every card, so the gaps in the tables below are real differences between GPUs, not run-to-run noise.
Compare single-slot GPU specs
Five columns change the buying decision for a single-slot card: memory, bandwidth, board power, whether it needs an auxiliary cable, and whether its encoder does AV1. Cooling decides which chassis it goes in, so that’s here too:
| Card | VRAM | Bandwidth | Board power | Aux power | AV1 encode | Cooling, height | Price band (check live) |
|---|---|---|---|---|---|---|---|
| RTX PRO 4000 Blackwell | 24 GB GDDR7 ECC | 672 GB/s | 145 W | 1x 16-pin | Yes, 2 NVENC | Blower, full height | about $3,075 to $3,100 |
| RTX A4000 | 16 GB GDDR6 ECC | 448 GB/s | 140 W | 1x 6-pin | No (decode only) | Blower, full height | about $1,326 to $1,417 |
| RTX 4000 Ada | 20 GB GDDR6 ECC | 360 GB/s | 130 W | Yes | Yes, 2 NVENC | Blower, full height | about $1,678 to $2,247 |
| L4 | 24 GB GDDR6 | 300 GB/s | 72 W | None | Yes, 2 NVENC | Passive, low profile | about $3,950 to $4,172 |
| RTX 2000E Ada | 16 GB GDDR6 ECC | 224 GB/s | 50 W | None | Yes, 1 NVENC | Fan, low profile | Integrator quote |
Bandwidth predicts LLM speed better than any other column. Generation speed divided by bandwidth came out almost flat across our three cards, at 0.16 to 0.18 tokens per second per GB/s on the 8B model. So you can estimate an untested card from its datasheet. By that maths the L4 lands below the RTX 4000 Ada for chat despite its 24 GB, and the 2000E Ada sits well below both.
Read the llama.cpp results
Here are the full numbers for both models, with peak board power taken during the run. Prompt processing (pp512) is how fast the card reads your prompt and documents; generation (tg128) is how fast it writes back:
| Card | 8B pp512 (t/s) | 8B tg128 (t/s) | 14B pp512 (t/s) | 14B tg128 (t/s) | Peak power |
|---|---|---|---|---|---|
| RTX PRO 4000 Blackwell | 5242.17 | 108.24 | 2724.93 | 60.87 | 145.3 W |
| RTX A4000 | 2820.06 | 74.00 | 1546.41 | 42.40 | 141.6 W |
| RTX 4000 Ada (115 W cap) | 3828.94 | 64.54 | 2043.41 | 36.48 | 115.3 W |
The surprise is the A4000 beating the newer RTX 4000 Ada on generation by 15 to 16 percent on both models. The Ada has the faster core, so it wins prompt processing by about a third. But its 160-bit bus gives it 360 GB/s against the A4000’s 448 GB/s, and streaming tokens is a memory-read problem. If you mostly chat with a model or run an agent loop, the older card is faster.
If you mostly feed it long documents for RAG or summarising, the Ada’s prompt speed matters more.
Efficiency reverses the price story. The RTX PRO 4000 Blackwell made about 0.75 tokens per second per watt on the 8B model, against 0.56 for the capped Ada and 0.53 for the A4000, using each card’s 8B-run peak. That number matters when four cards share one PSU and one airflow path. Cost per token goes the other way.
At the bottom of each price band the A4000 costs about $18 per token per second, the Ada about $26 and the PRO 4000 about $28. Our earlier RTX PRO 4000 vs RTX PRO 5000 benchmarks under Ollama landed in the same range (98.9 tokens per second on Llama 3.1 8B), so the llama.cpp figures are not a one-host fluke. The same tests also showed the 24 GB limit: under Ollama’s default context a 32B model at Q4_K_M spilled into system RAM and dropped to 5.3 tokens per second. Plan on 14B models for long context, or 32B only with a trimmed context.
Buy the RTX PRO 4000 Blackwell for AI
It’s the only card in this guide with 24 GB of memory and display outputs, and it won every test we ran by a wide margin. Four of them in a workstation give you 96 GB of ECC VRAM in four brackets.
On paper it carries 8,960 CUDA cores and 24 GB of GDDR7 on a 192-bit bus for 672 GB/s. Add PCIe 5.0 x16, four DisplayPort 2.1b outputs and two ninth-generation NVENC encoders with AV1. NVIDIA rates it at 145 W, which is what nvidia-smi reported as both the current and maximum limit on our rental. PNY’s page says 140 W. It takes one 16-pin 12V-2×6 cable, so check that your server PSU has enough of those before you order four.
- Buy it for 14B models with long context, or 70B across a four-card box
- Buy it for mixed AI and AV1 transcode duty on the same GPU
- Pass on it if your budget stops near $1,500 or your PSU has no 16-pin leads
Stock is thin and every Amazon offer we found was a third-party seller, so expect about $3,075 to $3,100 and check the live price before you plan a four-card build around it. NVIDIA’s RTX PRO 4000 product page lists the full specification if you need the dimensions for a riser.
Save with the RTX A4000
The A4000 is an older Ampere card, and it still generated tokens faster than the RTX 4000 Ada in our runs while costing less. It’s the card to buy when you want real CUDA performance in one slot and the money matters.
It has 6,144 CUDA cores and 16 GB of GDDR6 ECC on a 256-bit bus for 448 GB/s, on PCIe 4.0 x16 with four DisplayPort 1.4a outputs. At 140 W it needs only a single 6-pin plug on top of slot power, which almost every PSU already has. The 16 GB ceiling is the real limit. A 14B model at Q4_K_M fits with context to spare, but 32B models need a second card or CPU offload.
Transcoding is its weak side. The A4000 has one NVENC engine and no AV1 encoder, so it is fine for H.264 and HEVC in Plex or Jellyfin but cannot produce AV1 at all. Our ffmpeg test failed with the exact error shown in the transcoding section below. Pass on it if AV1 output is on your roadmap.
Otherwise expect about $1,326 to $1,417 new, and check the live listing, because one cheaper Amazon offer is titled “Renewed” while being flagged as new.
Pick the RTX 4000 Ada for AV1
The RTX 4000 Ada sits between the other two blowers: 20 GB of memory, the lowest power of the three and two AV1-capable encoders, with slower token generation than the A4000.
It packs 6,144 CUDA cores and 20 GB of GDDR6 ECC on a 160-bit bus for 360 GB/s, with four DisplayPort 1.4a outputs. At 130 W it needs an auxiliary cable. Our rental host capped it at 115 W, and it still beat the A4000 on prompt processing and encoded AV1 at an average of 211 fps from 4K to 1080p. So it fits a media server that also runs a small model, or a RAG box that reads more than it writes.
Its 20 GB buffer also buys room the A4000 doesn’t have, enough for a 14B model at Q8 or a 24B-class model at Q4 with short context. Pass on it if you only chat with models, because the A4000 is faster at that for less. The cheapest PNY retail listing was about $1,678 with stock running low, and other listings ran to $2,247, so check the live price.
Fit an NVIDIA L4 in 1U and 2U servers
The L4 is a half-height, half-length datacenter card at 72 W with no fan and no power connector. So it goes where blower cards physically can’t: a 1U riser, a low-profile 2U slot, or every slot of a dense chassis.
NVIDIA lists 24 GB of GDDR6 at 300 GB/s on PCIe 4.0 x16, with two NVENC, four NVDEC and four JPEG engines and AV1 encode and decode. It supports SR-IOV and vGPU and has no display outputs. Its heatsink is passive, so it depends on server fans pushing air front to back. In a desktop tower it overheats unless you add a fan shroud that forces air through it. The Amazon title says 75 W, but NVIDIA’s datasheet says 72 W, and the datasheet wins.
We did not benchmark it, but its 300 GB/s puts its chat speed below the RTX 4000 Ada by our bandwidth ratio. You buy it for 24 GB per low-profile slot, the encoder count and the vGPU licensing path, not for raw tokens. At about $3,950 to $4,172 it’s also the priciest card here, so pass on it unless the chassis forces it. If you’re still choosing the server, our guide to a first rackmount server for a homelab helps you pick the box before the card.
Run an RTX 2000E Ada without power leads
The 2000E Ada is low profile, single slot, actively cooled and powered entirely from the slot at 50 W. NVIDIA’s RTX A1000 (8 GB) and RTX A400 (4 GB) share that form factor, but the 2000E is the only one with 16 GB, which makes it the card in this guide for a small form factor office PC with no spare PSU leads.
PNY lists 2,816 CUDA cores, 16 GB of GDDR6 ECC on a 128-bit bus for 224 GB/s, PCIe 4.0 x8, four mini DisplayPort outputs, and both full-height and low-profile brackets in the box. Watch the name, because the plain RTX 2000 Ada is a dual-slot 70 W card and listings mix them up. We found no Amazon US listing for the 2000E, so it comes through PNY resellers and system integrators.
At 224 GB/s it is the slowest card here for LLMs, so treat it as a 7B or 8B model card, a light transcoder or a passthrough card for one desktop VM. If you need it to run 14B models at a usable speed, an A4000 in a full-height slot is the better buy.
Check the transcoding path before you buy
Hardware encode support is where single-slot cards split hardest, and the failure shows up only when you try it. Run an AV1 encode through ffmpeg’s NVENC path to find out what a card can do:
ffmpeg -hide_banner -hwaccel cuda -hwaccel_output_format cuda -i in4k.mp4 -vf scale_cuda=1920:1080 -c:v av1_nvenc -preset p4 -b:v 6M -f null -On the RTX A4000 the encoder refuses to open, because Ampere’s NVENC has no AV1 block:
[av1_nvenc @ 0x55a13e4cc500] Provided device doesn't support required NVENC features
[vost#0:0/av1_nvenc @ 0x55a13e4dbdc0] Error while opening encoder - maybe incorrect parameters such as bit_rate, rate, width or height.An identical command ran on the RTX 4000 Ada at an average of 211 fps (7.03x real time) and on the RTX PRO 4000 Blackwell at 504 fps (16.8x), both on a 20-second 4K clip scaled to 1080p. Those two used different ffmpeg builds on different hosts, so read them as “plenty of headroom” rather than a precise ranking. We left HEVC throughput out of the comparison entirely, because on a clip that short the encoder start-up time dominated the average.
The Blackwell card also taught us something no spec sheet mentions. On the first host, NVENC failed outright because the container didn’t expose NVIDIA’s encode library (libnvidia-encode), so no ffmpeg build could open the encoder. A second host with a newer driver and a current static ffmpeg build worked first time.
If you run Jellyfin or Plex in Docker, make sure the container sees that library (with the NVIDIA Container Toolkit, set NVIDIA_DRIVER_CAPABILITIES=compute,video,utility, because setting the variable replaces the default list), or a working card will look broken. Our GPU guide for Jellyfin and Plex transcoding covers stream counts and the transcoding side in more depth.
Two cheaper single-slot options come up in every homelab thread, and both carry caveats. NVIDIA’s product brief describes the used Tesla P4 as a single-slot, low-profile, passive 75 W card, and it sells for about $180 to $200. But it is Pascal, and NVIDIA’s R580 branch is the last Linux driver to support Pascal, so new drivers and CUDA releases will leave it behind.
Intel’s Sparkle Arc A310 ECO is single slot and low profile with AV1 at 50 W, but we could not find it on Amazon US and a recent Level1Techs thread reports the A310 being run out with prices up.
Plan power, airflow and PCIe lanes
A single-slot card fits because it gave something up, usually cooling headroom, and a dense build stacks that problem four times. Before you order, check these against your chassis and board:
- Blower cards (PRO 4000, A4000, 4000 Ada) exhaust out the bracket and stack touching
- Passive cards (L4, Tesla P4) need front-to-back server airflow
- Full height needs a full-height slot; the L4 and 2000E fit low-profile risers
- Aux leads: 16-pin for the PRO 4000, 6-pin for the A4000, one aux cable for the 4000 Ada, none for the L4 and 2000E
- Real x16 or x8 wiring behind each slot, not just the physical length
Lane wiring catches most four-card builds. Consumer boards often wire the third and fourth x16 slots as x4 or share them with M.2 sockets, which barely hurts inference once the model is loaded but slows model loading and any tensor-parallel traffic between cards. Our breakdown of PCIe lanes for GPU and NVMe builds shows how to read a board manual for this, and the multi-GPU motherboard guide lists boards that give each slot its own lanes.
Once the card is in, confirm the power limit and the maximum PCIe link the slot allows, because a riser or a shared slot can drop it a generation:
nvidia-smi --query-gpu=name,driver_version,memory.total,power.limit,power.max_limit,pcie.link.gen.max,pcie.link.width.max,clocks.max.sm --format=csvOur RTX 4000 Ada rental showed exactly that kind of shortfall, a 115 W limit against a 130 W maximum and a Gen 3 link:
name, driver_version, memory.total [MiB], power.limit [W], power.max_limit [W], pcie.link.gen.max, pcie.link.width.max, clocks.max.sm [MHz]
NVIDIA RTX 4000 Ada Generation, 580.173.02, 20475 MiB, 115.00 W, 130.00 W, 3, 16, 3105 MHzIf your own card reports a lower limit than its maximum, raise it with sudo nvidia-smi -pl 130 (use your card’s maximum) once you’ve confirmed the chassis can cool it. Run it on the host, not inside a container, and reapply it after every reboot or driver reload, because the limit does not persist. The nvidia-smi command guide explains the power columns and the -pl flag. If the card goes into a VM instead, the Proxmox GPU passthrough guide compares cards for that job.
Match the card to your chassis
Your chassis picks the shortlist, and the workload picks the card from it. Start from what you’re putting it in:
- Four-slot workstation for local LLMs: RTX PRO 4000 Blackwell, or A4000s on a budget
- Full-height 2U server for chat inference: RTX A4000
- Media server that also runs a model: RTX 4000 Ada
- 1U or low-profile 2U riser with server fans: L4
- Small office PC with no spare power leads: RTX 2000E Ada
If slot width is not actually your limit, a dual-slot consumer card gives you more speed per dollar, and the best GPU for LLMs guide ranks those on measured generation speed. Prices on workstation cards moved a lot this year, so check the current GPU prices tracker before you commit. For our money, one A4000 per full-height slot is still the build to beat, and the RTX PRO 4000 Blackwell is the upgrade once you need 24 GB per bracket.
