Meet the Underdog Saluki 27B: A 2-bit Qwen3.8-27B That Beats the Original at Tool Calling

Underdog, the on-device assistant from Conway Research, has released Saluki 27B under Apache 2.0. Underdog Saluki 27B is a 2-bit GGUF of Qwen3.8-27B that fits in 7.89 GB. The full BF16 model needs 54 GB. Underdog tuned the compression to protect tool calling, the skill that turns a chat model into an agent. For developers, that means a 27B-class agent model that runs in stock llama.cpp.

TL;DR

Size: 27B dense parameters. 7.89 GB GGUF versus 54 GB for BF16.
Runs on: stock llama.cpp and apps built on it, with full GPU offload. Optional 629 MB or 928 MB vision add-on.
Performance: 96% average retention across 9 benchmarks versus full Qwen3.8-27B.
Best: Parallel tool calls, 42 versus 35 for the full model (120% retention).
Worst: AIME 2025, 79.2 versus 96.7 (about 82% retention).
Bottom line:
- Best: beats the 54 GB original at tool calling in a sub-8 GB file.
- Worst: competition math and multi-step reasoning drop 12 to 18 points.

What is Underdog Saluki 27B?

Saluki 27B is a 2-bit, mixed-precision GGUF of Qwen3.8-27B built for local agents. It stacks 3 layers of work:

The base is Qwen3.8-27B, a dense 27B model from the Qwen team. It has 64 layers, mixes Gated DeltaNet linear attention with gated attention, and supports 262,144 tokens natively.
The second layer is ISTA-DASLab’s Qwen3.8-27B-GSQ-RCO-GGUF. GSQ learns accurate low-bit scalar grids per tensor. RCO assigns a quantization type to each tensor under a fixed size budget. ISTA’s smallest file, IQ2_XS, is 8.4 GB at 2.50 bits per weight.
The third layer is Underdog’s own pass. It shrank the file to 7.89 GB and targeted tool calling. The file is named IQ2-mix and carries an imatrix tag. Underdog has not published the full recipe for this pass.

How does Saluki perform on benchmarks?

Underdog splits its results into 2 groups.

The first group ran both models in the same harness:

Underdog Bench: 120 tasks from BFCL v4, frozen before testing. Thinking off, temperature 0. Saluki scores 88, the full model 84, and PrismML’s Bonsai 2 scores 70.
Parallel tool calls: 100 BFCL v4 parallel tasks with the official checker. Saluki 42, full model 35.
SWE-bench Verified: 50 issues. Saluki fixes 30, the full model 33.

The second group compares Saluki with public full-size scores:

Benchmark	Saluki 27B	Qwen3.8-27B (public)
IFEval (prompt-loose)	93.5	91.5
IFBench (prompt-loose)	72.7	71.0
MBPP+	78.0	83.9
MuSR	67.5	79.6
AIME 2025 (avg@4)	79.2	96.7
AIME 2026 (avg@4)	80.0	94.6

Base: Qwen3.8-27B, a dense 27B model. 64 layers mix Gated DeltaNet linear attention with gated attention. 262,144-token native context. BF16 weights need 54 GB.’,’ISTA-DASLab GSQ-RCO: GSQ learns low-bit scalar grids per tensor. RCO assigns a quant type to each tensor under a size budget. Smallest file: IQ2_XS, 2.50 bits per weight, 8.4 GB.’,’Underdog pass: shrank the file to 7.89 GB (IQ2-mix, imatrix) and targeted tool calling. Underdog has not published the full recipe. Runs in stock llama.cpp.’]; var st=q(‘.st’),D=document.getElementById(‘mtps-det’); function setS(i){st.forEach(function(s,j){s.classList.toggle(‘on’,j===i)});D.innerHTML=det[i];post()} st.forEach(function(s,i){s.onclick=function(){setS(i)}});setS(0); var G={a:[[‘Underdog Bench’,88,84,120,’of 120′],[‘Parallel tool calls’,42,35,100,’of 100′],[‘SWE-bench Verified’,30,33,50,’of 50′]], b:[[‘IFEval’,93.5,91.5,100,”],[‘IFBench’,72.7,71.0,100,”],[‘MBPP+’,78.0,83.9,100,”],[‘MuSR’,67.5,79.6,100,”],[‘AIME 2025’,79.2,96.7,100,”],[‘AIME 2026′,80.0,94.6,100,”]]}; var N={a:’Same harness and settings for both models. Underdog Bench: 120 BFCL v4 tasks, thinking off, temperature 0.’,b:’Full-size scores are public numbers from a different harness, which Underdog calls more lenient.’}; var BM=document.getElementById(‘mtps-bm’),BN=document.getElementById(‘mtps-bn’); function draw(g){var h=””;G[g].forEach(function(r){var ret=(r[1]/r[2]*100).toFixed(1);var c=r[1]>=r[2]?’up’:’dn’; h+=’<div class=""bm"">‘+r[0]+’ ‘+r[1]+’ vs ‘+r[2]+(r[4]?’ ‘+r[4]:”)+’‘+ret+’%</div>‘}); BM.innerHTML=h;BN.textContent=N[g]+’ Right column: Saluki as a share of the full model.’;grow(BM);setTimeout(post,80)} q(‘.seg button’).forEach(function(b){b.onclick=function(){q(‘.seg button’).forEach(function(x){x.classList.toggle(‘on’,x===b)});draw(b.getAttribute(‘data-g’))}});draw(‘a’); var RUN=[‘1. Download the 7.89 GB GGUF<pre>huggingface-cli download ConwayResearch/Underdog-Saluki-27B-1.0 Underdog-Saluki-27B-1.0-IQ2-mix.gguf --local-dir .</pre>‘, ‘2. Serve with stock llama.cpp<pre>llama-server -m Underdog-Saluki-27B-1.0-IQ2-mix.gguf --jinja -ngl 99 -fa on -c 32768</pre>–jinja turns on the Qwen3.8 chat template. OpenAI chat API on port 8080.‘, ‘3. Pick settingsGeneral use: thinking on, temperature 0.6, top_p 0.95, top_k 20. Fast tool calls: thinking off, temperature 0:<pre>"chat_template_kwargs": {"enable_thinking": false}</pre>Add –mmproj with the vision file for image input.‘]; var ri=0,RB=document.getElementById(‘mtps-run’),dots=q(‘.dot’),pv=document.getElementById(‘mtps-prev’),nx=document.getElementById(‘mtps-next’); function setR(i){ri=i;RB.innerHTML=RUN[i];dots.forEach(function(d,j){d.classList.toggle(‘on’,j===i)});pv.disabled=i===0;nx.disabled=i===2;post()} dots.forEach(function(d,i){d.onclick=function(){setR(i)}});pv.onclick=function(){if(ri>0)setR(ri-1)};nx.onclick=function(){if(ri<2)setR(ri+1)};setR(0); q(‘.lc’).forEach(function(c){c.onclick=function(){c.classList.toggle(‘open’);c.querySelector(‘.h span’).textContent=c.classList.contains(‘open’)?’-‘:’+’;setTimeout(post,320)}}); grow(R.querySelector(‘.pn.on’));window.addEventListener(‘load’,post);window.addEventListener(‘resize’,post);setTimeout(post,300); })();

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Meet the Underdog Saluki 27B: A 2-bit Qwen3.8-27B That Beats the Original at Tool Calling

TL;DR

What is Underdog Saluki 27B?

How does Saluki perform on benchmarks?

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